Overview

Dataset statistics

Number of variables115
Number of observations1137
Missing cells87738
Missing cells (%)67.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory959.5 KiB
Average record size in memory864.1 B

Variable types

Categorical21
DateTime1
Boolean67
Numeric26

Warnings

bleeding_other has constant value "False" Constant
chills has constant value "True" Constant
convulsions has constant value "False" Constant
cough has constant value "True" Constant
diarrhoea has constant value "True" Constant
event_enrolment has constant value "True" Constant
event_fever has constant value "True" Constant
event_pcr has constant value "True" Constant
feeling_faint has constant value "True" Constant
fluid_state has constant value "Study IPD" Constant
heart_sound has constant value "True" Constant
hematuria has constant value "False" Constant
jaundice has constant value "False" Constant
meningism has constant value "False" Constant
nausea has constant value "True" Constant
neurology_abnormal has constant value "False" Constant
other_blood_product has constant value "False" Constant
parental_fluid has constant value "True" Constant
pcr_dengue_interpretation has constant value "Lab-confirmed Dengue" Constant
pcr_dengue_serotype has constant value "Constant
retro_pain has constant value "True" Constant
sore_throat has constant value "True" Constant
spleen_palpation has constant value "False" Constant
study_no has a high cardinality: 75 distinct values High cardinality
medication_list has a high cardinality: 90 distinct values High cardinality
abdominal_pain_level is highly correlated with igg and 3 other fieldsHigh correlation
alt is highly correlated with body_temperature and 3 other fieldsHigh correlation
ast is highly correlated with body_temperature and 3 other fieldsHigh correlation
body_temperature is highly correlated with alt and 2 other fieldsHigh correlation
creatinine is highly correlated with body_temperature and 3 other fieldsHigh correlation
dbp is highly correlated with platelet_minHigh correlation
haematocrit_percent_lab is highly correlated with haematocrit_percent_max and 4 other fieldsHigh correlation
haematocrit_percent_max is highly correlated with haematocrit_percent_lab and 1 other fieldsHigh correlation
haematocrit_percent_min is highly correlated with haematocrit_percent_lab and 1 other fieldsHigh correlation
haemoglobin is highly correlated with haematocrit_percent_labHigh correlation
igg is highly correlated with abdominal_pain_level and 6 other fieldsHigh correlation
igm is highly correlated with abdominal_pain_level and 6 other fieldsHigh correlation
lymphocytes_percent is highly correlated with igg and 1 other fieldsHigh correlation
monocytes_percent is highly correlated with igg and 1 other fieldsHigh correlation
neutrophils_percent is highly correlated with igg and 1 other fieldsHigh correlation
pcr_dengue_load is highly correlated with abdominal_pain_levelHigh correlation
platelet_min is highly correlated with dbp and 3 other fieldsHigh correlation
pulse is highly correlated with alt and 2 other fieldsHigh correlation
respiratory_rate is highly correlated with alt and 3 other fieldsHigh correlation
sbp is highly correlated with alt and 2 other fieldsHigh correlation
vomiting_level is highly correlated with abdominal_pain_levelHigh correlation
wbc is highly correlated with igg and 1 other fieldsHigh correlation
abdominal_tenderness has 812 (71.4%) missing values Missing
albumin has 1073 (94.4%) missing values Missing
alt has 1056 (92.9%) missing values Missing
antibiotics has 882 (77.6%) missing values Missing
antibiotics_list has 1095 (96.3%) missing values Missing
ascites has 812 (71.4%) missing values Missing
ast has 1056 (92.9%) missing values Missing
bleeding has 883 (77.7%) missing values Missing
bleeding_gum has 810 (71.2%) missing values Missing
bleeding_nose has 810 (71.2%) missing values Missing
bleeding_other has 1066 (93.8%) missing values Missing
bleeding_vaginal has 844 (74.2%) missing values Missing
blood_fluid has 989 (87.0%) missing values Missing
body_temperature has 811 (71.3%) missing values Missing
bruising has 810 (71.2%) missing values Missing
chills has 1074 (94.5%) missing values Missing
colloid has 990 (87.1%) missing values Missing
colloid_description has 1127 (99.1%) missing values Missing
conjunctival_injection has 811 (71.3%) missing values Missing
convulsions has 883 (77.7%) missing values Missing
coronary_heart_disease has 1066 (93.8%) missing values Missing
cough has 1098 (96.6%) missing values Missing
creatine_kinase has 1101 (96.8%) missing values Missing
creatinine has 1055 (92.8%) missing values Missing
crystalloid has 989 (87.0%) missing values Missing
crystalloid_description has 995 (87.5%) missing values Missing
dbp has 1026 (90.2%) missing values Missing
diarrhoea has 1113 (97.9%) missing values Missing
event_enrolment has 1065 (93.7%) missing values Missing
event_fever has 1065 (93.7%) missing values Missing
event_pcr has 1065 (93.7%) missing values Missing
feeling_faint has 1130 (99.4%) missing values Missing
fluid_reason_other has 989 (87.0%) missing values Missing
fluid_reason_other_description has 1123 (98.8%) missing values Missing
fluid_state has 991 (87.2%) missing values Missing
haematocrit_high has 989 (87.0%) missing values Missing
haematocrit_percent_lab has 827 (72.7%) missing values Missing
haematocrit_percent_max has 1109 (97.5%) missing values Missing
haematocrit_percent_min has 1109 (97.5%) missing values Missing
haemoglobin has 810 (71.2%) missing values Missing
heart_sound has 1065 (93.7%) missing values Missing
hematemesis has 810 (71.2%) missing values Missing
hematuria has 810 (71.2%) missing values Missing
hypertension has 1065 (93.7%) missing values Missing
igg has 1009 (88.7%) missing values Missing
igg_interpretation has 1009 (88.7%) missing values Missing
igm has 1009 (88.7%) missing values Missing
igm_interpretation has 1009 (88.7%) missing values Missing
jaundice has 811 (71.3%) missing values Missing
lethargy has 810 (71.2%) missing values Missing
liver_palpation has 811 (71.3%) missing values Missing
lymphadenopathy has 812 (71.4%) missing values Missing
lymphadenopathy_specification has 1112 (97.8%) missing values Missing
lymphocytes_percent has 801 (70.4%) missing values Missing
medication has 883 (77.7%) missing values Missing
medication_list has 1000 (88.0%) missing values Missing
melaena has 810 (71.2%) missing values Missing
meningism has 882 (77.6%) missing values Missing
monocytes_percent has 801 (70.4%) missing values Missing
nausea has 1084 (95.3%) missing values Missing
neurology_abnormal has 810 (71.2%) missing values Missing
neutrophils_percent has 802 (70.5%) missing values Missing
oedema has 810 (71.2%) missing values Missing
other_blood_product has 991 (87.2%) missing values Missing
parental_fluid has 991 (87.2%) missing values Missing
parental_fluid_volume has 990 (87.1%) missing values Missing
pcr_dengue_load has 1065 (93.7%) missing values Missing
petechiae has 810 (71.2%) missing values Missing
pharyngeal_injection has 812 (71.4%) missing values Missing
platelet_min has 801 (70.4%) missing values Missing
platelets has 989 (87.0%) missing values Missing
pleural_effusion has 810 (71.2%) missing values Missing
pulse has 815 (71.7%) missing values Missing
rales_crackles has 810 (71.2%) missing values Missing
rehydration has 989 (87.0%) missing values Missing
renal_disease has 1065 (93.7%) missing values Missing
respiratory_distress has 810 (71.2%) missing values Missing
respiratory_rate has 812 (71.4%) missing values Missing
restlessness has 810 (71.2%) missing values Missing
retro_pain has 1112 (97.8%) missing values Missing
rhinitis has 811 (71.3%) missing values Missing
sbp has 811 (71.3%) missing values Missing
serology_interpretation has 1065 (93.7%) missing values Missing
shock_clinical has 882 (77.6%) missing values Missing
shock_resucitation has 989 (87.0%) missing values Missing
skin_describe has 1125 (98.9%) missing values Missing
skin_flush has 810 (71.2%) missing values Missing
skin_rash has 810 (71.2%) missing values Missing
sore_throat has 1116 (98.2%) missing values Missing
spleen_palpation has 812 (71.4%) missing values Missing
treatment_haemorrhage has 989 (87.0%) missing values Missing
vomiting has 989 (87.0%) missing values Missing
wbc has 801 (70.4%) missing values Missing
pcr_dengue_load has 21 (1.8%) zeros Zeros

Reproduction

Analysis started2021-01-24 10:20:08.558082
Analysis finished2021-01-24 10:21:14.443232
Duration1 minute and 5.89 seconds
Software versionpandas-profiling v2.10.0
Download configurationconfig.yaml

Variables

study_no
Categorical

HIGH CARDINALITY

Distinct75
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
20-0705
 
27
20-0701
 
23
20-0707
 
22
20-0706
 
21
20-0737
 
21
Other values (70)
1023 

Length

Max length9
Median length7
Mean length7.029903254
Min length7

Characters and Unicode

Total characters7993
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.3%

Sample

1st row20 - 0762
2nd row20 - 0762
3rd row20 - 0762
4th row20 - 0762
5th row20 - 0762
ValueCountFrequency (%)
20-070527
 
2.4%
20-070123
 
2.0%
20-070722
 
1.9%
20-070621
 
1.8%
20-073721
 
1.8%
20-070820
 
1.8%
20-071020
 
1.8%
20-076720
 
1.8%
20-075020
 
1.8%
20-073920
 
1.8%
Other values (65)923
81.2%
2021-01-24T11:21:14.633118image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
20-070527
 
2.3%
20-070123
 
2.0%
20-070722
 
1.9%
20-073721
 
1.8%
20-070621
 
1.8%
20-071020
 
1.7%
20-072120
 
1.7%
20-076820
 
1.7%
20-073920
 
1.7%
20-075020
 
1.7%
Other values (67)957
81.7%

Most occurring characters

ValueCountFrequency (%)
02545
31.8%
21439
18.0%
71341
16.8%
-1137
14.2%
1294
 
3.7%
6264
 
3.3%
3249
 
3.1%
5248
 
3.1%
4221
 
2.8%
8116
 
1.5%
Other values (2)139
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6822
85.3%
Dash Punctuation1137
 
14.2%
Space Separator34
 
0.4%

Most frequent character per category

ValueCountFrequency (%)
02545
37.3%
21439
21.1%
71341
19.7%
1294
 
4.3%
6264
 
3.9%
3249
 
3.6%
5248
 
3.6%
4221
 
3.2%
8116
 
1.7%
9105
 
1.5%
ValueCountFrequency (%)
34
100.0%
ValueCountFrequency (%)
-1137
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common7993
100.0%

Most frequent character per script

ValueCountFrequency (%)
02545
31.8%
21439
18.0%
71341
16.8%
-1137
14.2%
1294
 
3.7%
6264
 
3.3%
3249
 
3.1%
5248
 
3.1%
4221
 
2.8%
8116
 
1.5%
Other values (2)139
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII7993
100.0%

Most frequent character per block

ValueCountFrequency (%)
02545
31.8%
21439
18.0%
71341
16.8%
-1137
14.2%
1294
 
3.7%
6264
 
3.3%
3249
 
3.1%
5248
 
3.1%
4221
 
2.8%
8116
 
1.5%
Other values (2)139
 
1.7%

date
Date

Distinct845
Distinct (%)74.3%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
Minimum2013-06-26 00:00:00
Maximum2013-12-24 09:10:00
2021-01-24T11:21:14.750116image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:21:14.878226image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

abdominal_pain_level
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
nan
1113 
1.0
 
18
3.0
 
4
2.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3411
Distinct characters7
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownan
2nd rownan
3rd rownan
4th rownan
5th rownan
ValueCountFrequency (%)
nan1113
97.9%
1.018
 
1.6%
3.04
 
0.4%
2.02
 
0.2%
2021-01-24T11:21:15.716078image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-24T11:21:15.776406image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
nan1113
97.9%
1.018
 
1.6%
3.04
 
0.4%
2.02
 
0.2%

Most occurring characters

ValueCountFrequency (%)
n2226
65.3%
a1113
32.6%
.24
 
0.7%
024
 
0.7%
118
 
0.5%
34
 
0.1%
22
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3339
97.9%
Decimal Number48
 
1.4%
Other Punctuation24
 
0.7%

Most frequent character per category

ValueCountFrequency (%)
024
50.0%
118
37.5%
34
 
8.3%
22
 
4.2%
ValueCountFrequency (%)
n2226
66.7%
a1113
33.3%
ValueCountFrequency (%)
.24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3339
97.9%
Common72
 
2.1%

Most frequent character per script

ValueCountFrequency (%)
.24
33.3%
024
33.3%
118
25.0%
34
 
5.6%
22
 
2.8%
ValueCountFrequency (%)
n2226
66.7%
a1113
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII3411
100.0%

Most frequent character per block

ValueCountFrequency (%)
n2226
65.3%
a1113
32.6%
.24
 
0.7%
024
 
0.7%
118
 
0.5%
34
 
0.1%
22
 
0.1%

abdominal_tenderness
Boolean

MISSING

Distinct2
Distinct (%)0.6%
Missing812
Missing (%)71.4%
Memory size9.0 KiB
False
291 
True
 
34
(Missing)
812 
ValueCountFrequency (%)
False291
 
25.6%
True34
 
3.0%
(Missing)812
71.4%
2021-01-24T11:21:15.821991image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

age
Real number (ℝ≥0)

Distinct37
Distinct (%)3.3%
Missing3
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean30.12610229
Minimum5
Maximum64
Zeros0
Zeros (%)0.0%
Memory size9.0 KiB
2021-01-24T11:21:15.895086image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile12
Q120
median26
Q336
95-th percentile56
Maximum64
Range59
Interquartile range (IQR)16

Descriptive statistics

Standard deviation13.39181316
Coefficient of variation (CV)0.4445252501
Kurtosis-0.05678296686
Mean30.12610229
Median Absolute Deviation (MAD)7
Skewness0.8235984268
Sum34163
Variance179.3406596
MonotocityNot monotonic
2021-01-24T11:21:16.004763image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
19108
 
9.5%
2464
 
5.6%
2262
 
5.5%
2359
 
5.2%
2958
 
5.1%
3156
 
4.9%
3654
 
4.7%
5351
 
4.5%
3451
 
4.5%
2650
 
4.4%
Other values (27)521
45.8%
ValueCountFrequency (%)
511
1.0%
915
1.3%
1120
1.8%
1213
1.1%
1417
1.5%
ValueCountFrequency (%)
6423
2.0%
5914
1.2%
5816
1.4%
5618
1.6%
5518
1.6%

albumin
Real number (ℝ≥0)

MISSING

Distinct21
Distinct (%)32.8%
Missing1073
Missing (%)94.4%
Infinite0
Infinite (%)0.0%
Mean40.96875
Minimum24
Maximum51
Zeros0
Zeros (%)0.0%
Memory size9.0 KiB
2021-01-24T11:21:16.099266image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile28
Q137.75
median42
Q346
95-th percentile50
Maximum51
Range27
Interquartile range (IQR)8.25

Descriptive statistics

Standard deviation6.480664159
Coefficient of variation (CV)0.1581855477
Kurtosis0.3350358426
Mean40.96875
Median Absolute Deviation (MAD)4
Skewness-0.8170171315
Sum2622
Variance41.99900794
MonotocityNot monotonic
2021-01-24T11:21:16.201105image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
427
 
0.6%
476
 
0.5%
396
 
0.5%
435
 
0.4%
455
 
0.4%
464
 
0.4%
504
 
0.4%
373
 
0.3%
483
 
0.3%
383
 
0.3%
Other values (11)18
 
1.6%
(Missing)1073
94.4%
ValueCountFrequency (%)
241
0.1%
252
0.2%
282
0.2%
311
0.1%
321
0.1%
ValueCountFrequency (%)
511
 
0.1%
504
0.4%
483
0.3%
476
0.5%
464
0.4%

alt
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct54
Distinct (%)66.7%
Missing1056
Missing (%)92.9%
Infinite0
Infinite (%)0.0%
Mean61.35802469
Minimum8
Maximum437
Zeros0
Zeros (%)0.0%
Memory size9.0 KiB
2021-01-24T11:21:16.319165image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile15
Q129
median38
Q372
95-th percentile172
Maximum437
Range429
Interquartile range (IQR)43

Descriptive statistics

Standard deviation64.35221609
Coefficient of variation (CV)1.048798693
Kurtosis15.45445046
Mean61.35802469
Median Absolute Deviation (MAD)18
Skewness3.428885737
Sum4970
Variance4141.207716
MonotocityNot monotonic
2021-01-24T11:21:16.438097image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
345
 
0.4%
384
 
0.4%
204
 
0.4%
304
 
0.4%
294
 
0.4%
173
 
0.3%
932
 
0.2%
122
 
0.2%
262
 
0.2%
822
 
0.2%
Other values (44)49
 
4.3%
(Missing)1056
92.9%
ValueCountFrequency (%)
81
 
0.1%
122
0.2%
131
 
0.1%
151
 
0.1%
173
0.3%
ValueCountFrequency (%)
4371
0.1%
2891
0.1%
1981
0.1%
1801
0.1%
1721
0.1%

anemia
Boolean

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
False
1103 
True
 
34
ValueCountFrequency (%)
False1103
97.0%
True34
 
3.0%
2021-01-24T11:21:16.507879image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

anorexia
Boolean

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
True
995 
False
142 
ValueCountFrequency (%)
True995
87.5%
False142
 
12.5%
2021-01-24T11:21:16.546615image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

antibiotics
Boolean

MISSING

Distinct2
Distinct (%)0.8%
Missing882
Missing (%)77.6%
Memory size9.0 KiB
False
213 
True
 
42
(Missing)
882 
ValueCountFrequency (%)
False213
 
18.7%
True42
 
3.7%
(Missing)882
77.6%
2021-01-24T11:21:16.580887image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

antibiotics_list
Categorical

MISSING

Distinct17
Distinct (%)40.5%
Missing1095
Missing (%)96.3%
Memory size9.0 KiB
AZITHROMYCIN
6 
CEFTAZIDIME
4 
CEFOPERAZONE+ SUNBACTAM
4 
ROCEPHIN
4 
AMPICILIN+SULBACTAM
4 
Other values (12)
20 

Length

Max length36
Median length12
Mean length16.11904762
Min length8

Characters and Unicode

Total characters677
Distinct characters24
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)16.7%

Sample

1st rowCEFTRYAXON, AZITHROMYCIN,DOXYCYCLIN
2nd rowCEFTRIAXON, AZITHROMYCIN, DOXYCYCLIN
3rd rowCEFTRIAXON, AZITHROMYCIN, DOXYCYLIN
4th rowCEFTRIAXON, AZITHROMYCIN, DOXYCYCLIN
5th rowCEFTRIAXON
ValueCountFrequency (%)
AZITHROMYCIN6
 
0.5%
CEFTAZIDIME4
 
0.4%
CEFOPERAZONE+ SUNBACTAM4
 
0.4%
ROCEPHIN4
 
0.4%
AMPICILIN+SULBACTAM4
 
0.4%
AZITROMYCIN3
 
0.3%
SAVIAZIT3
 
0.3%
CEFTRIAXONE, DOXYCYCLIN3
 
0.3%
CEFTRIAXON, AZITHROMYCIN, DOXYCYCLIN2
 
0.2%
ERTAPENEM2
 
0.2%
Other values (7)7
 
0.6%
(Missing)1095
96.3%
2021-01-24T11:21:16.762330image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
azithromycin9
15.5%
doxycyclin6
10.3%
ceftazidime4
 
6.9%
ceftriaxon4
 
6.9%
rocephin4
 
6.9%
ampicilin+sulbactam4
 
6.9%
sunbactam4
 
6.9%
cefoperazone4
 
6.9%
saviazit3
 
5.2%
azitromycin3
 
5.2%
Other values (10)13
22.4%

Most occurring characters

ValueCountFrequency (%)
I75
 
11.1%
C63
 
9.3%
A62
 
9.2%
N49
 
7.2%
E47
 
6.9%
O45
 
6.6%
T40
 
5.9%
R34
 
5.0%
M33
 
4.9%
Y30
 
4.4%
Other values (14)199
29.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter640
94.5%
Space Separator16
 
2.4%
Other Punctuation13
 
1.9%
Math Symbol8
 
1.2%

Most frequent character per category

ValueCountFrequency (%)
I75
11.7%
C63
 
9.8%
A62
 
9.7%
N49
 
7.7%
E47
 
7.3%
O45
 
7.0%
T40
 
6.2%
R34
 
5.3%
M33
 
5.2%
Y30
 
4.7%
Other values (11)162
25.3%
ValueCountFrequency (%)
,13
100.0%
ValueCountFrequency (%)
16
100.0%
ValueCountFrequency (%)
+8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin640
94.5%
Common37
 
5.5%

Most frequent character per script

ValueCountFrequency (%)
I75
11.7%
C63
 
9.8%
A62
 
9.7%
N49
 
7.7%
E47
 
7.3%
O45
 
7.0%
T40
 
6.2%
R34
 
5.3%
M33
 
5.2%
Y30
 
4.7%
Other values (11)162
25.3%
ValueCountFrequency (%)
16
43.2%
,13
35.1%
+8
21.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII677
100.0%

Most frequent character per block

ValueCountFrequency (%)
I75
 
11.1%
C63
 
9.3%
A62
 
9.2%
N49
 
7.2%
E47
 
6.9%
O45
 
6.6%
T40
 
5.9%
R34
 
5.0%
M33
 
4.9%
Y30
 
4.4%
Other values (14)199
29.4%

ascites
Boolean

MISSING

Distinct2
Distinct (%)0.6%
Missing812
Missing (%)71.4%
Memory size9.0 KiB
False
309 
True
 
16
(Missing)
812 
ValueCountFrequency (%)
False309
 
27.2%
True16
 
1.4%
(Missing)812
71.4%
2021-01-24T11:21:16.826845image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

ast
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct58
Distinct (%)71.6%
Missing1056
Missing (%)92.9%
Infinite0
Infinite (%)0.0%
Mean85.7654321
Minimum17
Maximum831
Zeros0
Zeros (%)0.0%
Memory size9.0 KiB
2021-01-24T11:21:16.915006image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile20
Q126
median42
Q376
95-th percentile352
Maximum831
Range814
Interquartile range (IQR)50

Descriptive statistics

Standard deviation129.1645531
Coefficient of variation (CV)1.506021132
Kurtosis17.30934205
Mean85.7654321
Median Absolute Deviation (MAD)19
Skewness3.887441517
Sum6947
Variance16683.48179
MonotocityNot monotonic
2021-01-24T11:21:17.050319image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
265
 
0.4%
243
 
0.3%
323
 
0.3%
573
 
0.3%
342
 
0.2%
232
 
0.2%
252
 
0.2%
332
 
0.2%
462
 
0.2%
212
 
0.2%
Other values (48)55
 
4.8%
(Missing)1056
92.9%
ValueCountFrequency (%)
171
0.1%
181
0.1%
191
0.1%
202
0.2%
212
0.2%
ValueCountFrequency (%)
8311
0.1%
6301
0.1%
4081
0.1%
3901
0.1%
3521
0.1%

asthma
Boolean

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
False
1105 
True
 
32
ValueCountFrequency (%)
False1105
97.2%
True32
 
2.8%
2021-01-24T11:21:17.129946image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

bleeding
Boolean

MISSING

Distinct2
Distinct (%)0.8%
Missing883
Missing (%)77.7%
Memory size9.0 KiB
True
177 
False
 
77
(Missing)
883 
ValueCountFrequency (%)
True177
 
15.6%
False77
 
6.8%
(Missing)883
77.7%
2021-01-24T11:21:17.167936image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

bleeding_gum
Boolean

MISSING

Distinct2
Distinct (%)0.6%
Missing810
Missing (%)71.2%
Memory size9.0 KiB
False
302 
True
 
25
(Missing)
810 
ValueCountFrequency (%)
False302
 
26.6%
True25
 
2.2%
(Missing)810
71.2%
2021-01-24T11:21:17.204912image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

bleeding_nose
Boolean

MISSING

Distinct2
Distinct (%)0.6%
Missing810
Missing (%)71.2%
Memory size9.0 KiB
False
319 
True
 
8
(Missing)
810 
ValueCountFrequency (%)
False319
 
28.1%
True8
 
0.7%
(Missing)810
71.2%
2021-01-24T11:21:17.240404image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

bleeding_other
Boolean

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)1.4%
Missing1066
Missing (%)93.8%
Memory size9.0 KiB
False
 
71
(Missing)
1066 
ValueCountFrequency (%)
False71
 
6.2%
(Missing)1066
93.8%
2021-01-24T11:21:17.273410image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

bleeding_skin
Boolean

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
False
1129 
True
 
8
ValueCountFrequency (%)
False1129
99.3%
True8
 
0.7%
2021-01-24T11:21:17.303650image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

bleeding_vaginal
Boolean

MISSING

Distinct2
Distinct (%)0.7%
Missing844
Missing (%)74.2%
Memory size9.0 KiB
False
277 
True
 
16
(Missing)
844 
ValueCountFrequency (%)
False277
 
24.4%
True16
 
1.4%
(Missing)844
74.2%
2021-01-24T11:21:17.337351image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

blood_fluid
Boolean

MISSING

Distinct2
Distinct (%)1.4%
Missing989
Missing (%)87.0%
Memory size9.0 KiB
False
147 
True
 
1
(Missing)
989 
ValueCountFrequency (%)
False147
 
12.9%
True1
 
0.1%
(Missing)989
87.0%
2021-01-24T11:21:17.372199image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

body_temperature
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct45
Distinct (%)13.8%
Missing811
Missing (%)71.3%
Infinite0
Infinite (%)0.0%
Mean37.31717791
Minimum35.3
Maximum40
Zeros0
Zeros (%)0.0%
Memory size9.0 KiB
2021-01-24T11:21:17.444852image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum35.3
5-th percentile36
Q136.5
median37
Q338.2
95-th percentile39.5
Maximum40
Range4.7
Interquartile range (IQR)1.7

Descriptive statistics

Standard deviation1.128789418
Coefficient of variation (CV)0.03024852041
Kurtosis-0.6945074977
Mean37.31717791
Median Absolute Deviation (MAD)0.7
Skewness0.676671306
Sum12165.4
Variance1.27416555
MonotocityNot monotonic
2021-01-24T11:21:17.570140image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
36.538
 
3.3%
3728
 
2.5%
36.825
 
2.2%
3920
 
1.8%
3619
 
1.7%
36.217
 
1.5%
36.413
 
1.1%
39.512
 
1.1%
3811
 
1.0%
36.711
 
1.0%
Other values (35)132
 
11.6%
(Missing)811
71.3%
ValueCountFrequency (%)
35.32
 
0.2%
35.51
 
0.1%
35.61
 
0.1%
35.85
 
0.4%
3619
1.7%
ValueCountFrequency (%)
402
0.2%
39.91
 
0.1%
39.84
0.4%
39.71
 
0.1%
39.62
0.2%

bruising
Boolean

MISSING

Distinct2
Distinct (%)0.6%
Missing810
Missing (%)71.2%
Memory size9.0 KiB
True
192 
False
135 
(Missing)
810 
ValueCountFrequency (%)
True192
 
16.9%
False135
 
11.9%
(Missing)810
71.2%
2021-01-24T11:21:17.643198image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

chills
Boolean

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)1.6%
Missing1074
Missing (%)94.5%
Memory size9.0 KiB
True
 
63
(Missing)
1074 
ValueCountFrequency (%)
True63
 
5.5%
(Missing)1074
94.5%
2021-01-24T11:21:17.682873image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

chronic_hepatitis
Boolean

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
False
1118 
True
 
19
ValueCountFrequency (%)
False1118
98.3%
True19
 
1.7%
2021-01-24T11:21:17.714360image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

colloid
Boolean

MISSING

Distinct2
Distinct (%)1.4%
Missing990
Missing (%)87.1%
Memory size9.0 KiB
False
136 
True
 
11
(Missing)
990 
ValueCountFrequency (%)
False136
 
12.0%
True11
 
1.0%
(Missing)990
87.1%
2021-01-24T11:21:17.749437image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

colloid_description
Categorical

MISSING

Distinct4
Distinct (%)40.0%
Missing1127
Missing (%)99.1%
Memory size9.0 KiB
D60
5 
ALBUMIN
3 
REFORTAN 6%
1 
D60, ALBUMIN
1 

Length

Max length12
Median length5
Mean length5.9
Min length3

Characters and Unicode

Total characters59
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)20.0%

Sample

1st rowALBUMIN
2nd rowALBUMIN
3rd rowALBUMIN
4th rowREFORTAN 6%
5th rowD60, ALBUMIN
ValueCountFrequency (%)
D605
 
0.4%
ALBUMIN3
 
0.3%
REFORTAN 6%1
 
0.1%
D60, ALBUMIN1
 
0.1%
(Missing)1127
99.1%
2021-01-24T11:21:17.931841image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-24T11:21:17.991590image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
d606
50.0%
albumin4
33.3%
refortan1
 
8.3%
61
 
8.3%

Most occurring characters

ValueCountFrequency (%)
67
11.9%
D6
10.2%
06
10.2%
A5
8.5%
N5
8.5%
L4
 
6.8%
B4
 
6.8%
U4
 
6.8%
M4
 
6.8%
I4
 
6.8%
Other values (8)10
16.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter42
71.2%
Decimal Number13
 
22.0%
Space Separator2
 
3.4%
Other Punctuation2
 
3.4%

Most frequent character per category

ValueCountFrequency (%)
D6
14.3%
A5
11.9%
N5
11.9%
L4
9.5%
B4
9.5%
U4
9.5%
M4
9.5%
I4
9.5%
R2
 
4.8%
E1
 
2.4%
Other values (3)3
7.1%
ValueCountFrequency (%)
67
53.8%
06
46.2%
ValueCountFrequency (%)
%1
50.0%
,1
50.0%
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin42
71.2%
Common17
28.8%

Most frequent character per script

ValueCountFrequency (%)
D6
14.3%
A5
11.9%
N5
11.9%
L4
9.5%
B4
9.5%
U4
9.5%
M4
9.5%
I4
9.5%
R2
 
4.8%
E1
 
2.4%
Other values (3)3
7.1%
ValueCountFrequency (%)
67
41.2%
06
35.3%
2
 
11.8%
%1
 
5.9%
,1
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII59
100.0%

Most frequent character per block

ValueCountFrequency (%)
67
11.9%
D6
10.2%
06
10.2%
A5
8.5%
N5
8.5%
L4
 
6.8%
B4
 
6.8%
U4
 
6.8%
M4
 
6.8%
I4
 
6.8%
Other values (8)10
16.9%

conjunctival_injection
Boolean

MISSING

Distinct2
Distinct (%)0.6%
Missing811
Missing (%)71.3%
Memory size9.0 KiB
False
263 
True
 
63
(Missing)
811 
ValueCountFrequency (%)
False263
 
23.1%
True63
 
5.5%
(Missing)811
71.3%
2021-01-24T11:21:18.040974image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

convulsions
Boolean

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)0.4%
Missing883
Missing (%)77.7%
Memory size9.0 KiB
False
254 
(Missing)
883 
ValueCountFrequency (%)
False254
 
22.3%
(Missing)883
77.7%
2021-01-24T11:21:18.075031image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

coronary_heart_disease
Boolean

MISSING

Distinct2
Distinct (%)2.8%
Missing1066
Missing (%)93.8%
Memory size9.0 KiB
False
 
70
True
 
1
(Missing)
1066 
ValueCountFrequency (%)
False70
 
6.2%
True1
 
0.1%
(Missing)1066
93.8%
2021-01-24T11:21:18.108136image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

cough
Boolean

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)2.6%
Missing1098
Missing (%)96.6%
Memory size9.0 KiB
True
 
39
(Missing)
1098 
ValueCountFrequency (%)
True39
 
3.4%
(Missing)1098
96.6%
2021-01-24T11:21:18.140036image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

creatine_kinase
Real number (ℝ≥0)

MISSING

Distinct33
Distinct (%)91.7%
Missing1101
Missing (%)96.8%
Infinite0
Infinite (%)0.0%
Mean83.11111111
Minimum27
Maximum543
Zeros0
Zeros (%)0.0%
Memory size9.0 KiB
2021-01-24T11:21:18.200451image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile30
Q142.75
median57.5
Q375.75
95-th percentile199.25
Maximum543
Range516
Interquartile range (IQR)33

Descriptive statistics

Standard deviation104.4051936
Coefficient of variation (CV)1.256212222
Kurtosis14.63012228
Mean83.11111111
Median Absolute Deviation (MAD)17
Skewness3.848977529
Sum2992
Variance10900.44444
MonotocityNot monotonic
2021-01-24T11:21:18.312640image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
582
 
0.2%
352
 
0.2%
272
 
0.2%
631
 
0.1%
501
 
0.1%
651
 
0.1%
551
 
0.1%
711
 
0.1%
671
 
0.1%
5431
 
0.1%
Other values (23)23
 
2.0%
(Missing)1101
96.8%
ValueCountFrequency (%)
272
0.2%
311
0.1%
321
0.1%
352
0.2%
371
0.1%
ValueCountFrequency (%)
5431
0.1%
4491
0.1%
1161
0.1%
1001
0.1%
951
0.1%

creatinine
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct47
Distinct (%)57.3%
Missing1055
Missing (%)92.8%
Infinite0
Infinite (%)0.0%
Mean85.06097561
Minimum32
Maximum446
Zeros0
Zeros (%)0.0%
Memory size9.0 KiB
2021-01-24T11:21:18.421928image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum32
5-th percentile51.1
Q163
median74
Q389.25
95-th percentile112.9
Maximum446
Range414
Interquartile range (IQR)26.25

Descriptive statistics

Standard deviation59.23573904
Coefficient of variation (CV)0.696391484
Kurtosis29.51792775
Mean85.06097561
Median Absolute Deviation (MAD)12
Skewness5.21063045
Sum6975
Variance3508.872779
MonotocityNot monotonic
2021-01-24T11:21:18.548498image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
1065
 
0.4%
684
 
0.4%
784
 
0.4%
744
 
0.4%
613
 
0.3%
633
 
0.3%
753
 
0.3%
533
 
0.3%
832
 
0.2%
962
 
0.2%
Other values (37)49
 
4.3%
(Missing)1055
92.8%
ValueCountFrequency (%)
321
0.1%
391
0.1%
471
0.1%
501
0.1%
511
0.1%
ValueCountFrequency (%)
4461
0.1%
4241
0.1%
1441
0.1%
1361
0.1%
1131
0.1%

crystalloid
Boolean

MISSING

Distinct2
Distinct (%)1.4%
Missing989
Missing (%)87.0%
Memory size9.0 KiB
True
142 
False
 
6
(Missing)
989 
ValueCountFrequency (%)
True142
 
12.5%
False6
 
0.5%
(Missing)989
87.0%
2021-01-24T11:21:18.624228image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

crystalloid_description
Categorical

MISSING

Distinct15
Distinct (%)10.6%
Missing995
Missing (%)87.5%
Memory size9.0 KiB
RL
100 
NS
14 
NS, GLUCOSE 5%
 
6
RL,NS
 
6
DEXTROSE-NATRI
 
4
Other values (10)
12 

Length

Max length15
Median length2
Mean length3.647887324
Min length2

Characters and Unicode

Total characters518
Distinct characters19
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)5.6%

Sample

1st rowNS
2nd rowNS
3rd rowRL
4th rowRL
5th rowRL
ValueCountFrequency (%)
RL100
 
8.8%
NS14
 
1.2%
NS, GLUCOSE 5%6
 
0.5%
RL,NS6
 
0.5%
DEXTROSE-NATRI4
 
0.4%
NS,RL2
 
0.2%
NS,GLUCOSE 5%2
 
0.2%
RINGER, GLUCOSE1
 
0.1%
NS, RL1
 
0.1%
RINGERLACTAT1
 
0.1%
Other values (5)5
 
0.4%
(Missing)995
87.5%
2021-01-24T11:21:18.813839image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
rl103
63.6%
ns21
 
13.0%
glucose10
 
6.2%
510
 
6.2%
rl,ns6
 
3.7%
dextrose-natri4
 
2.5%
ns,rl2
 
1.2%
ns,glucose2
 
1.2%
ringer1
 
0.6%
nacl1
 
0.6%
Other values (2)2
 
1.2%

Most occurring characters

ValueCountFrequency (%)
L126
24.3%
R123
23.7%
S49
 
9.5%
N39
 
7.5%
E23
 
4.4%
,21
 
4.1%
20
 
3.9%
O17
 
3.3%
G15
 
2.9%
C15
 
2.9%
Other values (9)70
13.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter451
87.1%
Other Punctuation32
 
6.2%
Space Separator20
 
3.9%
Decimal Number11
 
2.1%
Dash Punctuation4
 
0.8%

Most frequent character per category

ValueCountFrequency (%)
L126
27.9%
R123
27.3%
S49
 
10.9%
N39
 
8.6%
E23
 
5.1%
O17
 
3.8%
G15
 
3.3%
C15
 
3.3%
U13
 
2.9%
T10
 
2.2%
Other values (4)21
 
4.7%
ValueCountFrequency (%)
,21
65.6%
%11
34.4%
ValueCountFrequency (%)
20
100.0%
ValueCountFrequency (%)
511
100.0%
ValueCountFrequency (%)
-4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin451
87.1%
Common67
 
12.9%

Most frequent character per script

ValueCountFrequency (%)
L126
27.9%
R123
27.3%
S49
 
10.9%
N39
 
8.6%
E23
 
5.1%
O17
 
3.8%
G15
 
3.3%
C15
 
3.3%
U13
 
2.9%
T10
 
2.2%
Other values (4)21
 
4.7%
ValueCountFrequency (%)
,21
31.3%
20
29.9%
511
16.4%
%11
16.4%
-4
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII518
100.0%

Most frequent character per block

ValueCountFrequency (%)
L126
24.3%
R123
23.7%
S49
 
9.5%
N39
 
7.5%
E23
 
4.4%
,21
 
4.1%
20
 
3.9%
O17
 
3.3%
G15
 
2.9%
C15
 
2.9%
Other values (9)70
13.5%

dbp
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct10
Distinct (%)9.0%
Missing1026
Missing (%)90.2%
Infinite0
Infinite (%)0.0%
Mean58.47747748
Minimum50
Maximum60
Zeros0
Zeros (%)0.0%
Memory size9.0 KiB
2021-01-24T11:21:18.919162image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile50
Q160
median60
Q360
95-th percentile60
Maximum60
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.272049252
Coefficient of variation (CV)0.05595400816
Kurtosis2.101458365
Mean58.47747748
Median Absolute Deviation (MAD)0
Skewness-1.93656783
Sum6491
Variance10.70630631
MonotocityNot monotonic
2021-01-24T11:21:19.014357image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
6087
 
7.7%
5010
 
0.9%
523
 
0.3%
583
 
0.3%
553
 
0.3%
571
 
0.1%
511
 
0.1%
531
 
0.1%
591
 
0.1%
561
 
0.1%
(Missing)1026
90.2%
ValueCountFrequency (%)
5010
0.9%
511
 
0.1%
523
 
0.3%
531
 
0.1%
553
 
0.3%
ValueCountFrequency (%)
6087
7.7%
591
 
0.1%
583
 
0.3%
571
 
0.1%
561
 
0.1%

diabetes
Boolean

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
False
1108 
True
 
29
ValueCountFrequency (%)
False1108
97.4%
True29
 
2.6%
2021-01-24T11:21:19.075261image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

diarrhoea
Boolean

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)4.2%
Missing1113
Missing (%)97.9%
Memory size9.0 KiB
True
 
24
(Missing)
1113 
ValueCountFrequency (%)
True24
 
2.1%
(Missing)1113
97.9%
2021-01-24T11:21:19.111295image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

event_enrolment
Boolean

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)1.4%
Missing1065
Missing (%)93.7%
Memory size9.0 KiB
True
 
72
(Missing)
1065 
ValueCountFrequency (%)
True72
 
6.3%
(Missing)1065
93.7%
2021-01-24T11:21:19.141385image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

event_fever
Boolean

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)1.4%
Missing1065
Missing (%)93.7%
Memory size9.0 KiB
True
 
72
(Missing)
1065 
ValueCountFrequency (%)
True72
 
6.3%
(Missing)1065
93.7%
2021-01-24T11:21:19.170973image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

event_pcr
Boolean

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)1.4%
Missing1065
Missing (%)93.7%
Memory size9.0 KiB
True
 
72
(Missing)
1065 
ValueCountFrequency (%)
True72
 
6.3%
(Missing)1065
93.7%
2021-01-24T11:21:19.200051image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

feeling_faint
Boolean

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)14.3%
Missing1130
Missing (%)99.4%
Memory size9.0 KiB
True
 
7
(Missing)
1130 
ValueCountFrequency (%)
True7
 
0.6%
(Missing)1130
99.4%
2021-01-24T11:21:19.228221image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

fluid_reason_other
Boolean

MISSING

Distinct2
Distinct (%)1.4%
Missing989
Missing (%)87.0%
Memory size9.0 KiB
False
135 
True
 
13
(Missing)
989 
ValueCountFrequency (%)
False135
 
11.9%
True13
 
1.1%
(Missing)989
87.0%
2021-01-24T11:21:19.257523image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

fluid_reason_other_description
Categorical

MISSING

Distinct7
Distinct (%)50.0%
Missing1123
Missing (%)98.8%
Memory size9.0 KiB
PHASE TRANSFER OF ANTIBIOTIC
4 
DIARRHEA
3 
SOLVENT ANTIBIOTIC
2 
SICK PATIENTS
2 
TRADITIONAL FORTEC
1 
Other values (2)
2 

Length

Max length28
Median length18
Mean length17.28571429
Min length7

Characters and Unicode

Total characters242
Distinct characters20
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)21.4%

Sample

1st rowSOLVENT ANTIBIOTIC
2nd rowSOLVENT ANTIBIOTIC
3rd rowALBUMIN
4th rowDECREASED PLATELETS
5th rowPHASE TRANSFER OF ANTIBIOTIC
ValueCountFrequency (%)
PHASE TRANSFER OF ANTIBIOTIC4
 
0.4%
DIARRHEA3
 
0.3%
SOLVENT ANTIBIOTIC2
 
0.2%
SICK PATIENTS2
 
0.2%
TRADITIONAL FORTEC1
 
0.1%
DECREASED PLATELETS1
 
0.1%
ALBUMIN1
 
0.1%
(Missing)1123
98.8%
2021-01-24T11:21:19.460474image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-24T11:21:19.526545image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
antibiotic6
18.8%
phase4
12.5%
transfer4
12.5%
of4
12.5%
diarrhea3
9.4%
solvent2
 
6.2%
patients2
 
6.2%
sick2
 
6.2%
decreased1
 
3.1%
traditional1
 
3.1%
Other values (3)3
9.4%

Most occurring characters

ValueCountFrequency (%)
I28
11.6%
T27
11.2%
A27
11.2%
E21
8.7%
18
 
7.4%
R17
 
7.0%
S16
 
6.6%
N16
 
6.6%
O14
 
5.8%
C10
 
4.1%
Other values (10)48
19.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter224
92.6%
Space Separator18
 
7.4%

Most frequent character per category

ValueCountFrequency (%)
I28
12.5%
T27
12.1%
A27
12.1%
E21
9.4%
R17
7.6%
S16
7.1%
N16
7.1%
O14
 
6.2%
C10
 
4.5%
F9
 
4.0%
Other values (9)39
17.4%
ValueCountFrequency (%)
18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin224
92.6%
Common18
 
7.4%

Most frequent character per script

ValueCountFrequency (%)
I28
12.5%
T27
12.1%
A27
12.1%
E21
9.4%
R17
7.6%
S16
7.1%
N16
7.1%
O14
 
6.2%
C10
 
4.5%
F9
 
4.0%
Other values (9)39
17.4%
ValueCountFrequency (%)
18
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII242
100.0%

Most frequent character per block

ValueCountFrequency (%)
I28
11.6%
T27
11.2%
A27
11.2%
E21
8.7%
18
 
7.4%
R17
 
7.0%
S16
 
6.6%
N16
 
6.6%
O14
 
5.8%
C10
 
4.1%
Other values (10)48
19.8%

fluid_state
Categorical

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)0.7%
Missing991
Missing (%)87.2%
Memory size9.0 KiB
Study IPD
146 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters1314
Distinct characters9
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowStudy IPD
2nd rowStudy IPD
3rd rowStudy IPD
4th rowStudy IPD
5th rowStudy IPD
ValueCountFrequency (%)
Study IPD146
 
12.8%
(Missing)991
87.2%
2021-01-24T11:21:19.716913image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-24T11:21:19.773642image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
study146
50.0%
ipd146
50.0%

Most occurring characters

ValueCountFrequency (%)
S146
11.1%
t146
11.1%
u146
11.1%
d146
11.1%
y146
11.1%
146
11.1%
I146
11.1%
P146
11.1%
D146
11.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter584
44.4%
Lowercase Letter584
44.4%
Space Separator146
 
11.1%

Most frequent character per category

ValueCountFrequency (%)
S146
25.0%
I146
25.0%
P146
25.0%
D146
25.0%
ValueCountFrequency (%)
t146
25.0%
u146
25.0%
d146
25.0%
y146
25.0%
ValueCountFrequency (%)
146
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1168
88.9%
Common146
 
11.1%

Most frequent character per script

ValueCountFrequency (%)
S146
12.5%
t146
12.5%
u146
12.5%
d146
12.5%
y146
12.5%
I146
12.5%
P146
12.5%
D146
12.5%
ValueCountFrequency (%)
146
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1314
100.0%

Most frequent character per block

ValueCountFrequency (%)
S146
11.1%
t146
11.1%
u146
11.1%
d146
11.1%
y146
11.1%
146
11.1%
I146
11.1%
P146
11.1%
D146
11.1%

gender
Categorical

Distinct2
Distinct (%)0.2%
Missing3
Missing (%)0.3%
Memory size9.0 KiB
Male
600 
Female
534 

Length

Max length6
Median length4
Mean length4.941798942
Min length4

Characters and Unicode

Total characters5604
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowMale
3rd rowMale
4th rowMale
5th rowMale
ValueCountFrequency (%)
Male600
52.8%
Female534
47.0%
(Missing)3
 
0.3%
2021-01-24T11:21:19.944892image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-24T11:21:20.007694image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
male600
52.9%
female534
47.1%

Most occurring characters

ValueCountFrequency (%)
e1668
29.8%
a1134
20.2%
l1134
20.2%
M600
 
10.7%
F534
 
9.5%
m534
 
9.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4470
79.8%
Uppercase Letter1134
 
20.2%

Most frequent character per category

ValueCountFrequency (%)
e1668
37.3%
a1134
25.4%
l1134
25.4%
m534
 
11.9%
ValueCountFrequency (%)
M600
52.9%
F534
47.1%

Most occurring scripts

ValueCountFrequency (%)
Latin5604
100.0%

Most frequent character per script

ValueCountFrequency (%)
e1668
29.8%
a1134
20.2%
l1134
20.2%
M600
 
10.7%
F534
 
9.5%
m534
 
9.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII5604
100.0%

Most frequent character per block

ValueCountFrequency (%)
e1668
29.8%
a1134
20.2%
l1134
20.2%
M600
 
10.7%
F534
 
9.5%
m534
 
9.5%

haematocrit_high
Boolean

MISSING

Distinct2
Distinct (%)1.4%
Missing989
Missing (%)87.0%
Memory size9.0 KiB
False
122 
True
 
26
(Missing)
989 
ValueCountFrequency (%)
False122
 
10.7%
True26
 
2.3%
(Missing)989
87.0%
2021-01-24T11:21:20.044776image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

haematocrit_percent_lab
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct149
Distinct (%)48.1%
Missing827
Missing (%)72.7%
Infinite0
Infinite (%)0.0%
Mean40.23580645
Minimum29.8
Maximum54
Zeros0
Zeros (%)0.0%
Memory size9.0 KiB
2021-01-24T11:21:20.121048image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum29.8
5-th percentile33.09
Q137.125
median40.05
Q343.475
95-th percentile47.2
Maximum54
Range24.2
Interquartile range (IQR)6.35

Descriptive statistics

Standard deviation4.391176998
Coefficient of variation (CV)0.1091360503
Kurtosis-0.2054915382
Mean40.23580645
Median Absolute Deviation (MAD)3.1
Skewness0.1349874539
Sum12473.1
Variance19.28243543
MonotocityNot monotonic
2021-01-24T11:21:20.245519image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38.18
 
0.7%
39.86
 
0.5%
425
 
0.4%
42.45
 
0.4%
40.75
 
0.4%
35.55
 
0.4%
39.15
 
0.4%
41.65
 
0.4%
41.25
 
0.4%
47.24
 
0.4%
Other values (139)257
 
22.6%
(Missing)827
72.7%
ValueCountFrequency (%)
29.81
0.1%
30.61
0.1%
31.11
0.1%
31.31
0.1%
31.41
0.1%
ValueCountFrequency (%)
541
0.1%
53.11
0.1%
51.61
0.1%
51.41
0.1%
49.21
0.1%

haematocrit_percent_max
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct25
Distinct (%)89.3%
Missing1109
Missing (%)97.5%
Infinite0
Infinite (%)0.0%
Mean46.48928571
Minimum21
Maximum57.4
Zeros0
Zeros (%)0.0%
Memory size9.0 KiB
2021-01-24T11:21:20.352065image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile38.915
Q144.825
median46.8
Q349.25
95-th percentile54.745
Maximum57.4
Range36.4
Interquartile range (IQR)4.425

Descriptive statistics

Standard deviation6.500047822
Coefficient of variation (CV)0.1398181908
Kurtosis8.4447154
Mean46.48928571
Median Absolute Deviation (MAD)2.2
Skewness-2.143951895
Sum1301.7
Variance42.25062169
MonotocityNot monotonic
2021-01-24T11:21:20.455451image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
46.12
 
0.2%
46.92
 
0.2%
44.92
 
0.2%
43.21
 
0.1%
501
 
0.1%
47.31
 
0.1%
491
 
0.1%
46.21
 
0.1%
50.61
 
0.1%
44.61
 
0.1%
Other values (15)15
 
1.3%
(Missing)1109
97.5%
ValueCountFrequency (%)
211
0.1%
37.21
0.1%
42.11
0.1%
42.91
0.1%
43.21
0.1%
ValueCountFrequency (%)
57.41
0.1%
55.91
0.1%
52.61
0.1%
52.21
0.1%
50.81
0.1%

haematocrit_percent_min
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct23
Distinct (%)82.1%
Missing1109
Missing (%)97.5%
Infinite0
Infinite (%)0.0%
Mean41.825
Minimum6.7
Maximum51.6
Zeros0
Zeros (%)0.0%
Memory size9.0 KiB
2021-01-24T11:21:20.553675image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum6.7
5-th percentile36.94
Q140.225
median42.5
Q344.6
95-th percentile49.625
Maximum51.6
Range44.9
Interquartile range (IQR)4.375

Descriptive statistics

Standard deviation7.761711581
Coefficient of variation (CV)0.1855758896
Kurtosis16.29667868
Mean41.825
Median Absolute Deviation (MAD)2.2
Skewness-3.518427749
Sum1171.1
Variance60.24416667
MonotocityNot monotonic
2021-01-24T11:21:20.648821image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
44.63
 
0.3%
42.52
 
0.2%
402
 
0.2%
40.32
 
0.2%
51.61
 
0.1%
39.91
 
0.1%
39.71
 
0.1%
49.81
 
0.1%
45.21
 
0.1%
41.21
 
0.1%
Other values (13)13
 
1.1%
(Missing)1109
97.5%
ValueCountFrequency (%)
6.71
0.1%
36.11
0.1%
38.51
0.1%
39.71
0.1%
39.91
0.1%
ValueCountFrequency (%)
51.61
0.1%
49.81
0.1%
49.31
0.1%
48.41
0.1%
45.91
0.1%

haemoglobin
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct76
Distinct (%)23.2%
Missing810
Missing (%)71.2%
Infinite0
Infinite (%)0.0%
Mean13.72446483
Minimum9.3
Maximum18.5
Zeros0
Zeros (%)0.0%
Memory size9.0 KiB
2021-01-24T11:21:20.752027image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum9.3
5-th percentile10.9
Q112.4
median13.7
Q315
95-th percentile16.3
Maximum18.5
Range9.2
Interquartile range (IQR)2.6

Descriptive statistics

Standard deviation1.737641976
Coefficient of variation (CV)0.126609088
Kurtosis-0.5091752858
Mean13.72446483
Median Absolute Deviation (MAD)1.3
Skewness-0.02426951743
Sum4487.9
Variance3.019399636
MonotocityNot monotonic
2021-01-24T11:21:20.875062image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.412
 
1.1%
14.912
 
1.1%
12.911
 
1.0%
15.411
 
1.0%
13.210
 
0.9%
11.710
 
0.9%
14.810
 
0.9%
159
 
0.8%
15.38
 
0.7%
11.98
 
0.7%
Other values (66)226
 
19.9%
(Missing)810
71.2%
ValueCountFrequency (%)
9.31
 
0.1%
9.61
 
0.1%
101
 
0.1%
10.11
 
0.1%
10.23
0.3%
ValueCountFrequency (%)
18.51
0.1%
18.31
0.1%
17.62
0.2%
17.41
0.1%
17.21
0.1%

headache_level
Categorical

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
nan
1073 
1.0
 
39
2.0
 
20
3.0
 
5

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3411
Distinct characters7
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd rownan
3rd rownan
4th rownan
5th rownan
ValueCountFrequency (%)
nan1073
94.4%
1.039
 
3.4%
2.020
 
1.8%
3.05
 
0.4%
2021-01-24T11:21:21.070259image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-24T11:21:21.127201image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
nan1073
94.4%
1.039
 
3.4%
2.020
 
1.8%
3.05
 
0.4%

Most occurring characters

ValueCountFrequency (%)
n2146
62.9%
a1073
31.5%
.64
 
1.9%
064
 
1.9%
139
 
1.1%
220
 
0.6%
35
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3219
94.4%
Decimal Number128
 
3.8%
Other Punctuation64
 
1.9%

Most frequent character per category

ValueCountFrequency (%)
064
50.0%
139
30.5%
220
 
15.6%
35
 
3.9%
ValueCountFrequency (%)
n2146
66.7%
a1073
33.3%
ValueCountFrequency (%)
.64
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3219
94.4%
Common192
 
5.6%

Most frequent character per script

ValueCountFrequency (%)
.64
33.3%
064
33.3%
139
20.3%
220
 
10.4%
35
 
2.6%
ValueCountFrequency (%)
n2146
66.7%
a1073
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII3411
100.0%

Most frequent character per block

ValueCountFrequency (%)
n2146
62.9%
a1073
31.5%
.64
 
1.9%
064
 
1.9%
139
 
1.1%
220
 
0.6%
35
 
0.1%

heart_sound
Boolean

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)1.4%
Missing1065
Missing (%)93.7%
Memory size9.0 KiB
True
 
72
(Missing)
1065 
ValueCountFrequency (%)
True72
 
6.3%
(Missing)1065
93.7%
2021-01-24T11:21:21.171458image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

height
Real number (ℝ≥0)

Distinct30
Distinct (%)2.6%
Missing3
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean160.8156966
Minimum110
Maximum180
Zeros0
Zeros (%)0.0%
Memory size9.0 KiB
2021-01-24T11:21:21.236314image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum110
5-th percentile145
Q1154
median162
Q3168
95-th percentile180
Maximum180
Range70
Interquartile range (IQR)14

Descriptive statistics

Standard deviation11.04649887
Coefficient of variation (CV)0.06869042701
Kurtosis3.335429803
Mean160.8156966
Median Absolute Deviation (MAD)7
Skewness-0.9980762444
Sum182365
Variance122.0251373
MonotocityNot monotonic
2021-01-24T11:21:21.341969image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
168104
 
9.1%
160101
 
8.9%
17086
 
7.6%
15084
 
7.4%
15269
 
6.1%
18067
 
5.9%
16357
 
5.0%
16252
 
4.6%
15550
 
4.4%
15441
 
3.6%
Other values (20)423
37.2%
ValueCountFrequency (%)
11011
 
1.0%
13015
 
1.3%
14538
3.3%
14613
 
1.1%
14720
1.8%
ValueCountFrequency (%)
18067
5.9%
17917
 
1.5%
17614
 
1.2%
17418
 
1.6%
17216
 
1.4%

hematemesis
Boolean

MISSING

Distinct2
Distinct (%)0.6%
Missing810
Missing (%)71.2%
Memory size9.0 KiB
False
326 
True
 
1
(Missing)
810 
ValueCountFrequency (%)
False326
28.7%
True1
 
0.1%
(Missing)810
71.2%
2021-01-24T11:21:21.408524image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

hematuria
Boolean

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)0.3%
Missing810
Missing (%)71.2%
Memory size9.0 KiB
False
327 
(Missing)
810 
ValueCountFrequency (%)
False327
28.8%
(Missing)810
71.2%
2021-01-24T11:21:21.443282image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

hypertension
Boolean

MISSING

Distinct2
Distinct (%)2.8%
Missing1065
Missing (%)93.7%
Memory size9.0 KiB
False
 
69
True
 
3
(Missing)
1065 
ValueCountFrequency (%)
False69
 
6.1%
True3
 
0.3%
(Missing)1065
93.7%
2021-01-24T11:21:21.474804image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

igg
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct112
Distinct (%)87.5%
Missing1009
Missing (%)88.7%
Infinite0
Infinite (%)0.0%
Mean17.35148438
Minimum0.78
Maximum28.21
Zeros0
Zeros (%)0.0%
Memory size9.0 KiB
2021-01-24T11:21:21.550021image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.78
5-th percentile1.23
Q17.2
median21.95
Q325.52
95-th percentile27.3855
Maximum28.21
Range27.43
Interquartile range (IQR)18.32

Descriptive statistics

Standard deviation9.52503978
Coefficient of variation (CV)0.5489466823
Kurtosis-1.163393544
Mean17.35148438
Median Absolute Deviation (MAD)4.57
Skewness-0.666192067
Sum2220.99
Variance90.72638282
MonotocityNot monotonic
2021-01-24T11:21:21.663813image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.183
 
0.3%
27.083
 
0.3%
6.442
 
0.2%
272
 
0.2%
1.342
 
0.2%
23.132
 
0.2%
18.222
 
0.2%
1.232
 
0.2%
22.882
 
0.2%
222
 
0.2%
Other values (102)106
 
9.3%
(Missing)1009
88.7%
ValueCountFrequency (%)
0.781
0.1%
0.791
0.1%
0.861
0.1%
1.031
0.1%
1.041
0.1%
ValueCountFrequency (%)
28.211
0.1%
28.121
0.1%
27.891
0.1%
27.881
0.1%
27.621
0.1%

igg_interpretation
Categorical

MISSING

Distinct3
Distinct (%)2.3%
Missing1009
Missing (%)88.7%
Memory size9.0 KiB
Positive
64 
Negative
46 
Equivocal
18 

Length

Max length9
Median length8
Mean length8.140625
Min length8

Characters and Unicode

Total characters1042
Distinct characters15
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPositive
2nd rowNegative
3rd rowNegative
4th rowNegative
5th rowNegative
ValueCountFrequency (%)
Positive64
 
5.6%
Negative46
 
4.0%
Equivocal18
 
1.6%
(Missing)1009
88.7%
2021-01-24T11:21:21.856141image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-24T11:21:21.914753image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
positive64
50.0%
negative46
35.9%
equivocal18
 
14.1%

Most occurring characters

ValueCountFrequency (%)
i192
18.4%
e156
15.0%
v128
12.3%
t110
10.6%
o82
7.9%
P64
 
6.1%
s64
 
6.1%
a64
 
6.1%
N46
 
4.4%
g46
 
4.4%
Other values (5)90
8.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter914
87.7%
Uppercase Letter128
 
12.3%

Most frequent character per category

ValueCountFrequency (%)
i192
21.0%
e156
17.1%
v128
14.0%
t110
12.0%
o82
9.0%
s64
 
7.0%
a64
 
7.0%
g46
 
5.0%
q18
 
2.0%
u18
 
2.0%
Other values (2)36
 
3.9%
ValueCountFrequency (%)
P64
50.0%
N46
35.9%
E18
 
14.1%

Most occurring scripts

ValueCountFrequency (%)
Latin1042
100.0%

Most frequent character per script

ValueCountFrequency (%)
i192
18.4%
e156
15.0%
v128
12.3%
t110
10.6%
o82
7.9%
P64
 
6.1%
s64
 
6.1%
a64
 
6.1%
N46
 
4.4%
g46
 
4.4%
Other values (5)90
8.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1042
100.0%

Most frequent character per block

ValueCountFrequency (%)
i192
18.4%
e156
15.0%
v128
12.3%
t110
10.6%
o82
7.9%
P64
 
6.1%
s64
 
6.1%
a64
 
6.1%
N46
 
4.4%
g46
 
4.4%
Other values (5)90
8.6%

igm
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct122
Distinct (%)95.3%
Missing1009
Missing (%)88.7%
Infinite0
Infinite (%)0.0%
Mean25.87273438
Minimum5.27
Maximum45.71
Zeros0
Zeros (%)0.0%
Memory size9.0 KiB
2021-01-24T11:21:22.000119image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum5.27
5-th percentile6.3895
Q113.5725
median26.81
Q337.3275
95-th percentile45.0995
Maximum45.71
Range40.44
Interquartile range (IQR)23.755

Descriptive statistics

Standard deviation13.44181229
Coefficient of variation (CV)0.5195358208
Kurtosis-1.425192115
Mean25.87273438
Median Absolute Deviation (MAD)12.12
Skewness-0.05696820729
Sum3311.71
Variance180.6823177
MonotocityNot monotonic
2021-01-24T11:21:22.120133image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45.682
 
0.2%
6.12
 
0.2%
45.082
 
0.2%
36.262
 
0.2%
26.812
 
0.2%
29.242
 
0.2%
37.321
 
0.1%
43.251
 
0.1%
35.491
 
0.1%
29.81
 
0.1%
Other values (112)112
 
9.9%
(Missing)1009
88.7%
ValueCountFrequency (%)
5.271
0.1%
5.561
0.1%
5.731
0.1%
6.12
0.2%
6.161
0.1%
ValueCountFrequency (%)
45.711
0.1%
45.682
0.2%
45.511
0.1%
45.341
0.1%
45.281
0.1%

igm_interpretation
Categorical

MISSING

Distinct3
Distinct (%)2.3%
Missing1009
Missing (%)88.7%
Memory size9.0 KiB
Positive
103 
Negative
21 
Equivocal
 
4

Length

Max length9
Median length8
Mean length8.03125
Min length8

Characters and Unicode

Total characters1028
Distinct characters15
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPositive
2nd rowNegative
3rd rowNegative
4th rowEquivocal
5th rowNegative
ValueCountFrequency (%)
Positive103
 
9.1%
Negative21
 
1.8%
Equivocal4
 
0.4%
(Missing)1009
88.7%
2021-01-24T11:21:22.325747image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-24T11:21:22.385050image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
positive103
80.5%
negative21
 
16.4%
equivocal4
 
3.1%

Most occurring characters

ValueCountFrequency (%)
i231
22.5%
e145
14.1%
v128
12.5%
t124
12.1%
o107
10.4%
P103
10.0%
s103
10.0%
a25
 
2.4%
N21
 
2.0%
g21
 
2.0%
Other values (5)20
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter900
87.5%
Uppercase Letter128
 
12.5%

Most frequent character per category

ValueCountFrequency (%)
i231
25.7%
e145
16.1%
v128
14.2%
t124
13.8%
o107
11.9%
s103
11.4%
a25
 
2.8%
g21
 
2.3%
q4
 
0.4%
u4
 
0.4%
Other values (2)8
 
0.9%
ValueCountFrequency (%)
P103
80.5%
N21
 
16.4%
E4
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Latin1028
100.0%

Most frequent character per script

ValueCountFrequency (%)
i231
22.5%
e145
14.1%
v128
12.5%
t124
12.1%
o107
10.4%
P103
10.0%
s103
10.0%
a25
 
2.4%
N21
 
2.0%
g21
 
2.0%
Other values (5)20
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1028
100.0%

Most frequent character per block

ValueCountFrequency (%)
i231
22.5%
e145
14.1%
v128
12.5%
t124
12.1%
o107
10.4%
P103
10.0%
s103
10.0%
a25
 
2.4%
N21
 
2.0%
g21
 
2.0%
Other values (5)20
 
1.9%

jaundice
Boolean

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)0.3%
Missing811
Missing (%)71.3%
Memory size9.0 KiB
False
326 
(Missing)
811 
ValueCountFrequency (%)
False326
28.7%
(Missing)811
71.3%
2021-01-24T11:21:22.426618image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

joint_pain_level
Categorical

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
nan
1095 
1.0
 
32
2.0
 
8
3.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3411
Distinct characters7
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd rownan
3rd rownan
4th rownan
5th rownan
ValueCountFrequency (%)
nan1095
96.3%
1.032
 
2.8%
2.08
 
0.7%
3.02
 
0.2%
2021-01-24T11:21:22.579355image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-24T11:21:22.634993image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
nan1095
96.3%
1.032
 
2.8%
2.08
 
0.7%
3.02
 
0.2%

Most occurring characters

ValueCountFrequency (%)
n2190
64.2%
a1095
32.1%
.42
 
1.2%
042
 
1.2%
132
 
0.9%
28
 
0.2%
32
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3285
96.3%
Decimal Number84
 
2.5%
Other Punctuation42
 
1.2%

Most frequent character per category

ValueCountFrequency (%)
042
50.0%
132
38.1%
28
 
9.5%
32
 
2.4%
ValueCountFrequency (%)
n2190
66.7%
a1095
33.3%
ValueCountFrequency (%)
.42
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3285
96.3%
Common126
 
3.7%

Most frequent character per script

ValueCountFrequency (%)
.42
33.3%
042
33.3%
132
25.4%
28
 
6.3%
32
 
1.6%
ValueCountFrequency (%)
n2190
66.7%
a1095
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII3411
100.0%

Most frequent character per block

ValueCountFrequency (%)
n2190
64.2%
a1095
32.1%
.42
 
1.2%
042
 
1.2%
132
 
0.9%
28
 
0.2%
32
 
0.1%

lethargy
Boolean

MISSING

Distinct2
Distinct (%)0.6%
Missing810
Missing (%)71.2%
Memory size9.0 KiB
False
323 
True
 
4
(Missing)
810 
ValueCountFrequency (%)
False323
 
28.4%
True4
 
0.4%
(Missing)810
71.2%
2021-01-24T11:21:22.680098image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

liver_palpation
Boolean

MISSING

Distinct2
Distinct (%)0.6%
Missing811
Missing (%)71.3%
Memory size9.0 KiB
False
310 
True
 
16
(Missing)
811 
ValueCountFrequency (%)
False310
 
27.3%
True16
 
1.4%
(Missing)811
71.3%
2021-01-24T11:21:22.715190image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

liver_size
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
nan
1121 
2.0
 
13
1.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3411
Distinct characters6
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownan
2nd rownan
3rd rownan
4th rownan
5th rownan
ValueCountFrequency (%)
nan1121
98.6%
2.013
 
1.1%
1.03
 
0.3%
2021-01-24T11:21:22.865547image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-24T11:21:22.921399image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
nan1121
98.6%
2.013
 
1.1%
1.03
 
0.3%

Most occurring characters

ValueCountFrequency (%)
n2242
65.7%
a1121
32.9%
.16
 
0.5%
016
 
0.5%
213
 
0.4%
13
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3363
98.6%
Decimal Number32
 
0.9%
Other Punctuation16
 
0.5%

Most frequent character per category

ValueCountFrequency (%)
016
50.0%
213
40.6%
13
 
9.4%
ValueCountFrequency (%)
n2242
66.7%
a1121
33.3%
ValueCountFrequency (%)
.16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3363
98.6%
Common48
 
1.4%

Most frequent character per script

ValueCountFrequency (%)
.16
33.3%
016
33.3%
213
27.1%
13
 
6.2%
ValueCountFrequency (%)
n2242
66.7%
a1121
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII3411
100.0%

Most frequent character per block

ValueCountFrequency (%)
n2242
65.7%
a1121
32.9%
.16
 
0.5%
016
 
0.5%
213
 
0.4%
13
 
0.1%

lymphadenopathy
Boolean

MISSING

Distinct2
Distinct (%)0.6%
Missing812
Missing (%)71.4%
Memory size9.0 KiB
False
300 
True
 
25
(Missing)
812 
ValueCountFrequency (%)
False300
 
26.4%
True25
 
2.2%
(Missing)812
71.4%
2021-01-24T11:21:22.960594image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

lymphadenopathy_specification
Categorical

MISSING

Distinct2
Distinct (%)8.0%
Missing1112
Missing (%)97.8%
Memory size9.0 KiB
1.0
18 
Cervical
7 

Length

Max length8
Median length3
Mean length4.4
Min length3

Characters and Unicode

Total characters110
Distinct characters11
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCervical
2nd row1.0
3rd row1.0
4th rowCervical
5th row1.0
ValueCountFrequency (%)
1.018
 
1.6%
Cervical7
 
0.6%
(Missing)1112
97.8%
2021-01-24T11:21:23.117552image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-24T11:21:23.174827image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
1.018
72.0%
cervical7
 
28.0%

Most occurring characters

ValueCountFrequency (%)
118
16.4%
.18
16.4%
018
16.4%
C7
 
6.4%
e7
 
6.4%
r7
 
6.4%
v7
 
6.4%
i7
 
6.4%
c7
 
6.4%
a7
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter49
44.5%
Decimal Number36
32.7%
Other Punctuation18
 
16.4%
Uppercase Letter7
 
6.4%

Most frequent character per category

ValueCountFrequency (%)
e7
14.3%
r7
14.3%
v7
14.3%
i7
14.3%
c7
14.3%
a7
14.3%
l7
14.3%
ValueCountFrequency (%)
118
50.0%
018
50.0%
ValueCountFrequency (%)
C7
100.0%
ValueCountFrequency (%)
.18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin56
50.9%
Common54
49.1%

Most frequent character per script

ValueCountFrequency (%)
C7
12.5%
e7
12.5%
r7
12.5%
v7
12.5%
i7
12.5%
c7
12.5%
a7
12.5%
l7
12.5%
ValueCountFrequency (%)
118
33.3%
.18
33.3%
018
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII110
100.0%

Most frequent character per block

ValueCountFrequency (%)
118
16.4%
.18
16.4%
018
16.4%
C7
 
6.4%
e7
 
6.4%
r7
 
6.4%
v7
 
6.4%
i7
 
6.4%
c7
 
6.4%
a7
 
6.4%

lymphocytes_percent
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct255
Distinct (%)75.9%
Missing801
Missing (%)70.4%
Infinite0
Infinite (%)0.0%
Mean40.9485119
Minimum4.1
Maximum77.7
Zeros0
Zeros (%)0.0%
Memory size9.0 KiB
2021-01-24T11:21:23.260332image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum4.1
5-th percentile14.675
Q129.05
median41.4
Q351.85
95-th percentile65.9
Maximum77.7
Range73.6
Interquartile range (IQR)22.8

Descriptive statistics

Standard deviation15.28842302
Coefficient of variation (CV)0.373357231
Kurtosis-0.6545189311
Mean40.9485119
Median Absolute Deviation (MAD)11.45
Skewness-0.03898324969
Sum13758.7
Variance233.7358784
MonotocityNot monotonic
2021-01-24T11:21:23.384456image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45.74
 
0.4%
19.54
 
0.4%
43.13
 
0.3%
49.93
 
0.3%
40.63
 
0.3%
383
 
0.3%
613
 
0.3%
403
 
0.3%
52.53
 
0.3%
56.73
 
0.3%
Other values (245)304
 
26.7%
(Missing)801
70.4%
ValueCountFrequency (%)
4.11
0.1%
4.81
0.1%
6.41
0.1%
9.41
0.1%
101
0.1%
ValueCountFrequency (%)
77.71
0.1%
71.51
0.1%
70.52
0.2%
70.31
0.1%
70.21
0.1%

medication
Boolean

MISSING

Distinct2
Distinct (%)0.8%
Missing883
Missing (%)77.7%
Memory size9.0 KiB
True
135 
False
119 
(Missing)
883 
ValueCountFrequency (%)
True135
 
11.9%
False119
 
10.5%
(Missing)883
77.7%
2021-01-24T11:21:23.457846image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

medication_list
Categorical

HIGH CARDINALITY
MISSING

Distinct90
Distinct (%)65.7%
Missing1000
Missing (%)88.0%
Memory size9.0 KiB
A
24 
H: PANTOPRAZOL
 
5
A,H: SMECTA
 
4
H;LORATADIN
 
3
H:GASTROLIUM, PANTOPRAZOLE,ACID TRANEXAMID
 
3
Other values (85)
98 

Length

Max length55
Median length14
Mean length16.15328467
Min length1

Characters and Unicode

Total characters2213
Distinct characters32
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique73 ?
Unique (%)53.3%

Sample

1st rowH; PIRACETAM, CIGENOL,VITAMIN C
2nd rowA, H:PIRACETAM
3rd rowA, H: PIRACETAM
4th rowH: PIRACETAM, OMEPRAZOL
5th rowH: PIRACETAM, OMEPRAZOLE
ValueCountFrequency (%)
A24
 
2.1%
H: PANTOPRAZOL5
 
0.4%
A,H: SMECTA4
 
0.4%
H;LORATADIN3
 
0.3%
H:GASTROLIUM, PANTOPRAZOLE,ACID TRANEXAMID3
 
0.3%
F3
 
0.3%
H: DIPHENHYDRAMIN HYDROCLORID2
 
0.2%
A,H: DOXYCYCLIN, DOSPAMIN2
 
0.2%
H: DOXYCYCLIN,2
 
0.2%
D2
 
0.2%
Other values (80)87
 
7.7%
(Missing)1000
88.0%
2021-01-24T11:21:23.673872image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
h40
 
15.7%
a32
 
12.5%
a,h11
 
4.3%
pantoprazol9
 
3.5%
piracetam5
 
2.0%
smecta5
 
2.0%
omeprazole5
 
2.0%
d5
 
2.0%
diosmectit4
 
1.6%
omeprazol4
 
1.6%
Other values (76)135
52.9%

Most occurring characters

ValueCountFrequency (%)
A290
 
13.1%
O167
 
7.5%
I137
 
6.2%
E134
 
6.1%
R126
 
5.7%
119
 
5.4%
T117
 
5.3%
H116
 
5.2%
L111
 
5.0%
N104
 
4.7%
Other values (22)792
35.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1894
85.6%
Other Punctuation197
 
8.9%
Space Separator119
 
5.4%
Decimal Number2
 
0.1%
Dash Punctuation1
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
A290
15.3%
O167
 
8.8%
I137
 
7.2%
E134
 
7.1%
R126
 
6.7%
T117
 
6.2%
H116
 
6.1%
L111
 
5.9%
N104
 
5.5%
M99
 
5.2%
Other values (14)493
26.0%
ValueCountFrequency (%)
,102
51.8%
:75
38.1%
;18
 
9.1%
.2
 
1.0%
ValueCountFrequency (%)
11
50.0%
01
50.0%
ValueCountFrequency (%)
119
100.0%
ValueCountFrequency (%)
-1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1894
85.6%
Common319
 
14.4%

Most frequent character per script

ValueCountFrequency (%)
A290
15.3%
O167
 
8.8%
I137
 
7.2%
E134
 
7.1%
R126
 
6.7%
T117
 
6.2%
H116
 
6.1%
L111
 
5.9%
N104
 
5.5%
M99
 
5.2%
Other values (14)493
26.0%
ValueCountFrequency (%)
119
37.3%
,102
32.0%
:75
23.5%
;18
 
5.6%
.2
 
0.6%
-1
 
0.3%
11
 
0.3%
01
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2213
100.0%

Most frequent character per block

ValueCountFrequency (%)
A290
 
13.1%
O167
 
7.5%
I137
 
6.2%
E134
 
6.1%
R126
 
5.7%
119
 
5.4%
T117
 
5.3%
H116
 
5.2%
L111
 
5.0%
N104
 
4.7%
Other values (22)792
35.8%

melaena
Boolean

MISSING

Distinct2
Distinct (%)0.6%
Missing810
Missing (%)71.2%
Memory size9.0 KiB
False
326 
True
 
1
(Missing)
810 
ValueCountFrequency (%)
False326
28.7%
True1
 
0.1%
(Missing)810
71.2%
2021-01-24T11:21:23.738848image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

meningism
Boolean

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)0.4%
Missing882
Missing (%)77.6%
Memory size9.0 KiB
False
255 
(Missing)
882 
ValueCountFrequency (%)
False255
 
22.4%
(Missing)882
77.6%
2021-01-24T11:21:23.776324image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

monocytes_percent
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct179
Distinct (%)53.3%
Missing801
Missing (%)70.4%
Infinite0
Infinite (%)0.0%
Mean16.47797619
Minimum2.4
Maximum60.9
Zeros0
Zeros (%)0.0%
Memory size9.0 KiB
2021-01-24T11:21:23.850577image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum2.4
5-th percentile6.65
Q110.7
median14.85
Q319.025
95-th percentile33.575
Maximum60.9
Range58.5
Interquartile range (IQR)8.325

Descriptive statistics

Standard deviation8.823932352
Coefficient of variation (CV)0.5354985497
Kurtosis5.189403125
Mean16.47797619
Median Absolute Deviation (MAD)4.15
Skewness1.944976531
Sum5536.6
Variance77.86178216
MonotocityNot monotonic
2021-01-24T11:21:23.970034image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.66
 
0.5%
13.95
 
0.4%
14.85
 
0.4%
11.95
 
0.4%
135
 
0.4%
14.94
 
0.4%
124
 
0.4%
9.84
 
0.4%
11.24
 
0.4%
9.24
 
0.4%
Other values (169)290
 
25.5%
(Missing)801
70.4%
ValueCountFrequency (%)
2.41
0.1%
2.61
0.1%
4.61
0.1%
4.81
0.1%
5.41
0.1%
ValueCountFrequency (%)
60.91
0.1%
53.31
0.1%
52.31
0.1%
51.22
0.2%
47.91
0.1%

muscle_pain_level
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
nan
1073 
1.0
 
50
2.0
 
14

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3411
Distinct characters6
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd rownan
3rd rownan
4th rownan
5th rownan
ValueCountFrequency (%)
nan1073
94.4%
1.050
 
4.4%
2.014
 
1.2%
2021-01-24T11:21:24.165966image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-24T11:21:24.218829image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
nan1073
94.4%
1.050
 
4.4%
2.014
 
1.2%

Most occurring characters

ValueCountFrequency (%)
n2146
62.9%
a1073
31.5%
.64
 
1.9%
064
 
1.9%
150
 
1.5%
214
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3219
94.4%
Decimal Number128
 
3.8%
Other Punctuation64
 
1.9%

Most frequent character per category

ValueCountFrequency (%)
064
50.0%
150
39.1%
214
 
10.9%
ValueCountFrequency (%)
n2146
66.7%
a1073
33.3%
ValueCountFrequency (%)
.64
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3219
94.4%
Common192
 
5.6%

Most frequent character per script

ValueCountFrequency (%)
.64
33.3%
064
33.3%
150
26.0%
214
 
7.3%
ValueCountFrequency (%)
n2146
66.7%
a1073
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII3411
100.0%

Most frequent character per block

ValueCountFrequency (%)
n2146
62.9%
a1073
31.5%
.64
 
1.9%
064
 
1.9%
150
 
1.5%
214
 
0.4%

nausea
Boolean

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)1.9%
Missing1084
Missing (%)95.3%
Memory size9.0 KiB
True
 
53
(Missing)
1084 
ValueCountFrequency (%)
True53
 
4.7%
(Missing)1084
95.3%
2021-01-24T11:21:24.258458image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

neurology_abnormal
Boolean

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)0.3%
Missing810
Missing (%)71.2%
Memory size9.0 KiB
False
327 
(Missing)
810 
ValueCountFrequency (%)
False327
28.8%
(Missing)810
71.2%
2021-01-24T11:21:24.287924image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

neutrophils_percent
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct255
Distinct (%)76.1%
Missing802
Missing (%)70.5%
Infinite0
Infinite (%)0.0%
Mean40.27044776
Minimum10.5
Maximum90.2
Zeros0
Zeros (%)0.0%
Memory size9.0 KiB
2021-01-24T11:21:24.363746image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum10.5
5-th percentile15.97
Q126.95
median38.5
Q351.15
95-th percentile71.86
Maximum90.2
Range79.7
Interquartile range (IQR)24.2

Descriptive statistics

Standard deviation17.22055044
Coefficient of variation (CV)0.4276225222
Kurtosis-0.4646441371
Mean40.27044776
Median Absolute Deviation (MAD)12.4
Skewness0.4512504643
Sum13490.6
Variance296.5473576
MonotocityNot monotonic
2021-01-24T11:21:24.489837image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28.84
 
0.4%
213
 
0.3%
25.53
 
0.3%
31.53
 
0.3%
35.73
 
0.3%
25.13
 
0.3%
38.63
 
0.3%
55.63
 
0.3%
46.13
 
0.3%
58.93
 
0.3%
Other values (245)304
 
26.7%
(Missing)802
70.5%
ValueCountFrequency (%)
10.51
0.1%
10.71
0.1%
11.12
0.2%
11.62
0.2%
11.81
0.1%
ValueCountFrequency (%)
90.21
0.1%
86.51
0.1%
81.21
0.1%
79.52
0.2%
78.32
0.2%

oedema
Boolean

MISSING

Distinct2
Distinct (%)0.6%
Missing810
Missing (%)71.2%
Memory size9.0 KiB
False
318 
True
 
9
(Missing)
810 
ValueCountFrequency (%)
False318
 
28.0%
True9
 
0.8%
(Missing)810
71.2%
2021-01-24T11:21:24.566292image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

other_blood_product
Boolean

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)0.7%
Missing991
Missing (%)87.2%
Memory size9.0 KiB
False
146 
(Missing)
991 
ValueCountFrequency (%)
False146
 
12.8%
(Missing)991
87.2%
2021-01-24T11:21:24.602214image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

parental_fluid
Boolean

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)0.7%
Missing991
Missing (%)87.2%
Memory size9.0 KiB
True
146 
(Missing)
991 
ValueCountFrequency (%)
True146
 
12.8%
(Missing)991
87.2%
2021-01-24T11:21:24.631342image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

parental_fluid_volume
Real number (ℝ≥0)

MISSING

Distinct60
Distinct (%)40.8%
Missing990
Missing (%)87.1%
Infinite0
Infinite (%)0.0%
Mean1092.306122
Minimum100
Maximum5850
Zeros0
Zeros (%)0.0%
Memory size9.0 KiB
2021-01-24T11:21:24.705858image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile334.1
Q1638
median1000
Q31000
95-th percentile2526.1
Maximum5850
Range5750
Interquartile range (IQR)362

Descriptive statistics

Standard deviation765.2319338
Coefficient of variation (CV)0.7005654533
Kurtosis11.56937855
Mean1092.306122
Median Absolute Deviation (MAD)120
Skewness2.803169917
Sum160569
Variance585579.9125
MonotocityNot monotonic
2021-01-24T11:21:24.834037image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100061
 
5.4%
50020
 
1.8%
35003
 
0.3%
8202
 
0.2%
10602
 
0.2%
8802
 
0.2%
12502
 
0.2%
15002
 
0.2%
1002
 
0.2%
6771
 
0.1%
Other values (50)50
 
4.4%
(Missing)990
87.1%
ValueCountFrequency (%)
1002
0.2%
1501
0.1%
1871
0.1%
2001
0.1%
2501
0.1%
ValueCountFrequency (%)
58501
 
0.1%
36001
 
0.1%
35003
0.3%
30251
 
0.1%
28331
 
0.1%

pcr_dengue_interpretation
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
Lab-confirmed Dengue
1137 

Length

Max length20
Median length20
Mean length20
Min length20

Characters and Unicode

Total characters22740
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLab-confirmed Dengue
2nd rowLab-confirmed Dengue
3rd rowLab-confirmed Dengue
4th rowLab-confirmed Dengue
5th rowLab-confirmed Dengue
ValueCountFrequency (%)
Lab-confirmed Dengue1137
100.0%
2021-01-24T11:21:25.018614image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-24T11:21:25.071022image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
lab-confirmed1137
50.0%
dengue1137
50.0%

Most occurring characters

ValueCountFrequency (%)
e3411
15.0%
n2274
 
10.0%
L1137
 
5.0%
a1137
 
5.0%
b1137
 
5.0%
-1137
 
5.0%
c1137
 
5.0%
o1137
 
5.0%
f1137
 
5.0%
i1137
 
5.0%
Other values (7)7959
35.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter18192
80.0%
Uppercase Letter2274
 
10.0%
Dash Punctuation1137
 
5.0%
Space Separator1137
 
5.0%

Most frequent character per category

ValueCountFrequency (%)
e3411
18.8%
n2274
12.5%
a1137
 
6.2%
b1137
 
6.2%
c1137
 
6.2%
o1137
 
6.2%
f1137
 
6.2%
i1137
 
6.2%
r1137
 
6.2%
m1137
 
6.2%
Other values (3)3411
18.8%
ValueCountFrequency (%)
L1137
50.0%
D1137
50.0%
ValueCountFrequency (%)
-1137
100.0%
ValueCountFrequency (%)
1137
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin20466
90.0%
Common2274
 
10.0%

Most frequent character per script

ValueCountFrequency (%)
e3411
16.7%
n2274
 
11.1%
L1137
 
5.6%
a1137
 
5.6%
b1137
 
5.6%
c1137
 
5.6%
o1137
 
5.6%
f1137
 
5.6%
i1137
 
5.6%
r1137
 
5.6%
Other values (5)5685
27.8%
ValueCountFrequency (%)
-1137
50.0%
1137
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII22740
100.0%

Most frequent character per block

ValueCountFrequency (%)
e3411
15.0%
n2274
 
10.0%
L1137
 
5.0%
a1137
 
5.0%
b1137
 
5.0%
-1137
 
5.0%
c1137
 
5.0%
o1137
 
5.0%
f1137
 
5.0%
i1137
 
5.0%
Other values (7)7959
35.0%

pcr_dengue_load
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct52
Distinct (%)72.2%
Missing1065
Missing (%)93.7%
Infinite0
Infinite (%)0.0%
Mean161132294.9
Minimum0
Maximum4080000000
Zeros21
Zeros (%)1.8%
Memory size9.0 KiB
2021-01-24T11:21:25.155239image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median144500
Q37728571.75
95-th percentile595500000
Maximum4080000000
Range4080000000
Interquartile range (IQR)7728571.75

Descriptive statistics

Standard deviation678494348.1
Coefficient of variation (CV)4.210790572
Kurtosis28.62976761
Mean161132294.9
Median Absolute Deviation (MAD)144500
Skewness5.331735494
Sum1.160152523 × 1010
Variance4.603545804 × 1017
MonotocityNot monotonic
2021-01-24T11:21:25.287963image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
021
 
1.8%
429761901
 
0.1%
6010000001
 
0.1%
2130951
 
0.1%
17000001
 
0.1%
573001
 
0.1%
60714291
 
0.1%
21900001
 
0.1%
127000001
 
0.1%
7980001
 
0.1%
Other values (42)42
 
3.7%
(Missing)1065
93.7%
ValueCountFrequency (%)
021
1.8%
17401
 
0.1%
30601
 
0.1%
35801
 
0.1%
47201
 
0.1%
ValueCountFrequency (%)
40800000001
0.1%
39300000001
0.1%
12559523811
0.1%
6010000001
0.1%
5910000001
0.1%

pcr_dengue_serotype
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
<LOD
1137 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters4548
Distinct characters4
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<LOD
2nd row<LOD
3rd row<LOD
4th row<LOD
5th row<LOD
ValueCountFrequency (%)
<LOD1137
100.0%
2021-01-24T11:21:25.486154image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-24T11:21:25.536555image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
lod1137
100.0%

Most occurring characters

ValueCountFrequency (%)
<1137
25.0%
L1137
25.0%
O1137
25.0%
D1137
25.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter3411
75.0%
Math Symbol1137
 
25.0%

Most frequent character per category

ValueCountFrequency (%)
L1137
33.3%
O1137
33.3%
D1137
33.3%
ValueCountFrequency (%)
<1137
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3411
75.0%
Common1137
 
25.0%

Most frequent character per script

ValueCountFrequency (%)
L1137
33.3%
O1137
33.3%
D1137
33.3%
ValueCountFrequency (%)
<1137
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII4548
100.0%

Most frequent character per block

ValueCountFrequency (%)
<1137
25.0%
L1137
25.0%
O1137
25.0%
D1137
25.0%

peptic_ulcer
Boolean

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
False
992 
True
145 
ValueCountFrequency (%)
False992
87.2%
True145
 
12.8%
2021-01-24T11:21:25.567967image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

petechiae
Boolean

MISSING

Distinct2
Distinct (%)0.6%
Missing810
Missing (%)71.2%
Memory size9.0 KiB
False
255 
True
 
72
(Missing)
810 
ValueCountFrequency (%)
False255
 
22.4%
True72
 
6.3%
(Missing)810
71.2%
2021-01-24T11:21:25.605120image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

pharyngeal_injection
Boolean

MISSING

Distinct2
Distinct (%)0.6%
Missing812
Missing (%)71.4%
Memory size9.0 KiB
False
285 
True
 
40
(Missing)
812 
ValueCountFrequency (%)
False285
 
25.1%
True40
 
3.5%
(Missing)812
71.4%
2021-01-24T11:21:25.639073image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

platelet_min
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct168
Distinct (%)50.0%
Missing801
Missing (%)70.4%
Infinite0
Infinite (%)0.0%
Mean103.5145833
Minimum9
Maximum687
Zeros0
Zeros (%)0.0%
Memory size9.0 KiB
2021-01-24T11:21:25.713723image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile15.75
Q139.75
median72
Q3123
95-th percentile321.25
Maximum687
Range678
Interquartile range (IQR)83.25

Descriptive statistics

Standard deviation99.51898999
Coefficient of variation (CV)0.9614006721
Kurtosis6.503782754
Mean103.5145833
Median Absolute Deviation (MAD)40
Skewness2.277463184
Sum34780.9
Variance9904.029369
MonotocityNot monotonic
2021-01-24T11:21:25.840843image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
227
 
0.6%
637
 
0.6%
847
 
0.6%
586
 
0.5%
306
 
0.5%
696
 
0.5%
885
 
0.4%
175
 
0.4%
1084
 
0.4%
414
 
0.4%
Other values (158)279
 
24.5%
(Missing)801
70.4%
ValueCountFrequency (%)
92
0.2%
102
0.2%
112
0.2%
123
0.3%
13.91
 
0.1%
ValueCountFrequency (%)
6871
0.1%
5321
0.1%
5032
0.2%
4391
0.1%
4332
0.2%

platelets
Boolean

MISSING

Distinct2
Distinct (%)1.4%
Missing989
Missing (%)87.0%
Memory size9.0 KiB
False
138 
True
 
10
(Missing)
989 
ValueCountFrequency (%)
False138
 
12.1%
True10
 
0.9%
(Missing)989
87.0%
2021-01-24T11:21:25.916612image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

pleural_effusion
Boolean

MISSING

Distinct2
Distinct (%)0.6%
Missing810
Missing (%)71.2%
Memory size9.0 KiB
False
308 
True
 
19
(Missing)
810 
ValueCountFrequency (%)
False308
 
27.1%
True19
 
1.7%
(Missing)810
71.2%
2021-01-24T11:21:25.955079image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

pregnant
Boolean

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
False
1110 
True
 
27
ValueCountFrequency (%)
False1110
97.6%
True27
 
2.4%
2021-01-24T11:21:25.990199image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

pulse
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct55
Distinct (%)17.1%
Missing815
Missing (%)71.7%
Infinite0
Infinite (%)0.0%
Mean80.13043478
Minimum52
Maximum125
Zeros0
Zeros (%)0.0%
Memory size9.0 KiB
2021-01-24T11:21:26.068133image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum52
5-th percentile60.1
Q172
median80
Q388
95-th percentile100
Maximum125
Range73
Interquartile range (IQR)16

Descriptive statistics

Standard deviation12.17685381
Coefficient of variation (CV)0.151962907
Kurtosis0.2604565193
Mean80.13043478
Median Absolute Deviation (MAD)8
Skewness0.4032746933
Sum25802
Variance148.2757687
MonotocityNot monotonic
2021-01-24T11:21:26.191632image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8034
 
3.0%
7024
 
2.1%
7518
 
1.6%
8816
 
1.4%
9013
 
1.1%
7813
 
1.1%
8512
 
1.1%
7412
 
1.1%
659
 
0.8%
848
 
0.7%
Other values (45)163
 
14.3%
(Missing)815
71.7%
ValueCountFrequency (%)
521
 
0.1%
551
 
0.1%
561
 
0.1%
573
0.3%
582
0.2%
ValueCountFrequency (%)
1251
0.1%
1181
0.1%
1141
0.1%
1102
0.2%
1091
0.1%

rales_crackles
Boolean

MISSING

Distinct2
Distinct (%)0.6%
Missing810
Missing (%)71.2%
Memory size9.0 KiB
False
322 
True
 
5
(Missing)
810 
ValueCountFrequency (%)
False322
 
28.3%
True5
 
0.4%
(Missing)810
71.2%
2021-01-24T11:21:26.264218image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

rehydration
Boolean

MISSING

Distinct2
Distinct (%)1.4%
Missing989
Missing (%)87.0%
Memory size9.0 KiB
True
136 
False
 
12
(Missing)
989 
ValueCountFrequency (%)
True136
 
12.0%
False12
 
1.1%
(Missing)989
87.0%
2021-01-24T11:21:26.299091image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

renal_disease
Boolean

MISSING

Distinct2
Distinct (%)2.8%
Missing1065
Missing (%)93.7%
Memory size9.0 KiB
False
 
71
True
 
1
(Missing)
1065 
ValueCountFrequency (%)
False71
 
6.2%
True1
 
0.1%
(Missing)1065
93.7%
2021-01-24T11:21:26.334200image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

respiratory_distress
Boolean

MISSING

Distinct2
Distinct (%)0.6%
Missing810
Missing (%)71.2%
Memory size9.0 KiB
False
323 
True
 
4
(Missing)
810 
ValueCountFrequency (%)
False323
 
28.4%
True4
 
0.4%
(Missing)810
71.2%
2021-01-24T11:21:26.366847image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

respiratory_rate
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct13
Distinct (%)4.0%
Missing812
Missing (%)71.4%
Infinite0
Infinite (%)0.0%
Mean19.65846154
Minimum15
Maximum28
Zeros0
Zeros (%)0.0%
Memory size9.0 KiB
2021-01-24T11:21:26.421827image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile18
Q118
median20
Q320
95-th percentile23
Maximum28
Range13
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.797708788
Coefficient of variation (CV)0.09144707403
Kurtosis2.232740174
Mean19.65846154
Median Absolute Deviation (MAD)1
Skewness1.104787419
Sum6389
Variance3.231756885
MonotocityNot monotonic
2021-01-24T11:21:26.516184image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
20137
 
12.0%
18104
 
9.1%
2224
 
2.1%
1923
 
2.0%
2310
 
0.9%
248
 
0.7%
215
 
0.4%
164
 
0.4%
173
 
0.3%
253
 
0.3%
Other values (3)4
 
0.4%
(Missing)812
71.4%
ValueCountFrequency (%)
151
 
0.1%
164
 
0.4%
173
 
0.3%
18104
9.1%
1923
 
2.0%
ValueCountFrequency (%)
281
 
0.1%
262
 
0.2%
253
 
0.3%
248
0.7%
2310
0.9%

restlessness
Boolean

MISSING

Distinct2
Distinct (%)0.6%
Missing810
Missing (%)71.2%
Memory size9.0 KiB
False
324 
True
 
3
(Missing)
810 
ValueCountFrequency (%)
False324
 
28.5%
True3
 
0.3%
(Missing)810
71.2%
2021-01-24T11:21:26.575390image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

retro_pain
Boolean

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)4.0%
Missing1112
Missing (%)97.8%
Memory size9.0 KiB
True
 
25
(Missing)
1112 
ValueCountFrequency (%)
True25
 
2.2%
(Missing)1112
97.8%
2021-01-24T11:21:26.614616image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

rhinitis
Boolean

MISSING

Distinct2
Distinct (%)0.6%
Missing811
Missing (%)71.3%
Memory size9.0 KiB
False
321 
True
 
5
(Missing)
811 
ValueCountFrequency (%)
False321
 
28.2%
True5
 
0.4%
(Missing)811
71.3%
2021-01-24T11:21:26.646137image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

sbp
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct63
Distinct (%)19.3%
Missing811
Missing (%)71.3%
Infinite0
Infinite (%)0.0%
Mean98.76380368
Minimum50
Maximum150
Zeros0
Zeros (%)0.0%
Memory size9.0 KiB
2021-01-24T11:21:26.727391image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile60
Q190
median100
Q3110
95-th percentile130
Maximum150
Range100
Interquartile range (IQR)20

Descriptive statistics

Standard deviation20.43246383
Coefficient of variation (CV)0.2068821073
Kurtosis-0.2560608251
Mean98.76380368
Median Absolute Deviation (MAD)10
Skewness-0.494139485
Sum32197
Variance417.4855781
MonotocityNot monotonic
2021-01-24T11:21:26.845142image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10062
 
5.5%
11054
 
4.7%
12023
 
2.0%
9017
 
1.5%
13013
 
1.1%
6012
 
1.1%
708
 
0.7%
1058
 
0.7%
985
 
0.4%
855
 
0.4%
Other values (53)119
 
10.5%
(Missing)811
71.3%
ValueCountFrequency (%)
502
0.2%
551
 
0.1%
572
0.2%
583
0.3%
594
0.4%
ValueCountFrequency (%)
1502
0.2%
1451
0.1%
1361
0.1%
1351
0.1%
1332
0.2%

serology_interpretation
Categorical

MISSING

Distinct3
Distinct (%)4.2%
Missing1065
Missing (%)93.7%
Memory size9.0 KiB
Probable Secondary
35 
Inconclusive
31 
Probable primary
6 

Length

Max length18
Median length16
Mean length15.25
Min length12

Characters and Unicode

Total characters1098
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowInconclusive
2nd rowInconclusive
3rd rowInconclusive
4th rowInconclusive
5th rowInconclusive
ValueCountFrequency (%)
Probable Secondary35
 
3.1%
Inconclusive31
 
2.7%
Probable primary6
 
0.5%
(Missing)1065
93.7%
2021-01-24T11:21:27.050452image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-24T11:21:27.109377image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
probable41
36.3%
secondary35
31.0%
inconclusive31
27.4%
primary6
 
5.3%

Most occurring characters

ValueCountFrequency (%)
o107
 
9.7%
e107
 
9.7%
n97
 
8.8%
c97
 
8.8%
r88
 
8.0%
b82
 
7.5%
a82
 
7.5%
l72
 
6.6%
P41
 
3.7%
41
 
3.7%
Other values (10)284
25.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter950
86.5%
Uppercase Letter107
 
9.7%
Space Separator41
 
3.7%

Most frequent character per category

ValueCountFrequency (%)
o107
11.3%
e107
11.3%
n97
10.2%
c97
10.2%
r88
9.3%
b82
8.6%
a82
8.6%
l72
7.6%
y41
 
4.3%
i37
 
3.9%
Other values (6)140
14.7%
ValueCountFrequency (%)
P41
38.3%
S35
32.7%
I31
29.0%
ValueCountFrequency (%)
41
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1057
96.3%
Common41
 
3.7%

Most frequent character per script

ValueCountFrequency (%)
o107
10.1%
e107
10.1%
n97
 
9.2%
c97
 
9.2%
r88
 
8.3%
b82
 
7.8%
a82
 
7.8%
l72
 
6.8%
P41
 
3.9%
y41
 
3.9%
Other values (9)243
23.0%
ValueCountFrequency (%)
41
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1098
100.0%

Most frequent character per block

ValueCountFrequency (%)
o107
 
9.7%
e107
 
9.7%
n97
 
8.8%
c97
 
8.8%
r88
 
8.0%
b82
 
7.5%
a82
 
7.5%
l72
 
6.6%
P41
 
3.7%
41
 
3.7%
Other values (10)284
25.9%

shock_clinical
Boolean

MISSING

Distinct2
Distinct (%)0.8%
Missing882
Missing (%)77.6%
Memory size9.0 KiB
False
254 
True
 
1
(Missing)
882 
ValueCountFrequency (%)
False254
 
22.3%
True1
 
0.1%
(Missing)882
77.6%
2021-01-24T11:21:27.152926image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

shock_resucitation
Boolean

MISSING

Distinct2
Distinct (%)1.4%
Missing989
Missing (%)87.0%
Memory size9.0 KiB
False
144 
True
 
4
(Missing)
989 
ValueCountFrequency (%)
False144
 
12.7%
True4
 
0.4%
(Missing)989
87.0%
2021-01-24T11:21:27.188807image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

skin_describe
Categorical

MISSING

Distinct5
Distinct (%)41.7%
Missing1125
Missing (%)98.9%
Memory size9.0 KiB
MACULAR
5 
RECOVERY
3 
MACULOPAPULAR
2 
MACULER
1 
MACULAR
1 

Length

Max length13
Median length7.5
Mean length8.333333333
Min length7

Characters and Unicode

Total characters100
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)16.7%

Sample

1st rowMACULOPAPULAR
2nd rowMACULOPAPULAR
3rd rowMACULAR
4th rowRECOVERY
5th rowMACULAR
ValueCountFrequency (%)
MACULAR5
 
0.4%
RECOVERY3
 
0.3%
MACULOPAPULAR2
 
0.2%
MACULER1
 
0.1%
MACULAR 1
 
0.1%
(Missing)1125
98.9%
2021-01-24T11:21:27.371618image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-24T11:21:27.435753image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
macular6
50.0%
recovery3
25.0%
maculopapular2
 
16.7%
maculer1
 
8.3%

Most occurring characters

ValueCountFrequency (%)
A19
19.0%
R15
15.0%
C12
12.0%
U11
11.0%
L11
11.0%
M9
9.0%
E7
 
7.0%
O5
 
5.0%
P4
 
4.0%
V3
 
3.0%
Other values (2)4
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter99
99.0%
Space Separator1
 
1.0%

Most frequent character per category

ValueCountFrequency (%)
A19
19.2%
R15
15.2%
C12
12.1%
U11
11.1%
L11
11.1%
M9
9.1%
E7
 
7.1%
O5
 
5.1%
P4
 
4.0%
V3
 
3.0%
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin99
99.0%
Common1
 
1.0%

Most frequent character per script

ValueCountFrequency (%)
A19
19.2%
R15
15.2%
C12
12.1%
U11
11.1%
L11
11.1%
M9
9.1%
E7
 
7.1%
O5
 
5.1%
P4
 
4.0%
V3
 
3.0%
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII100
100.0%

Most frequent character per block

ValueCountFrequency (%)
A19
19.0%
R15
15.0%
C12
12.0%
U11
11.0%
L11
11.0%
M9
9.0%
E7
 
7.0%
O5
 
5.0%
P4
 
4.0%
V3
 
3.0%
Other values (2)4
 
4.0%

skin_flush
Boolean

MISSING

Distinct2
Distinct (%)0.6%
Missing810
Missing (%)71.2%
Memory size9.0 KiB
False
171 
True
156 
(Missing)
810 
ValueCountFrequency (%)
False171
 
15.0%
True156
 
13.7%
(Missing)810
71.2%
2021-01-24T11:21:27.485361image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

skin_rash
Boolean

MISSING

Distinct2
Distinct (%)0.6%
Missing810
Missing (%)71.2%
Memory size9.0 KiB
False
226 
True
101 
(Missing)
810 
ValueCountFrequency (%)
False226
 
19.9%
True101
 
8.9%
(Missing)810
71.2%
2021-01-24T11:21:28.413661image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

sore_throat
Boolean

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)4.8%
Missing1116
Missing (%)98.2%
Memory size9.0 KiB
True
 
21
(Missing)
1116 
ValueCountFrequency (%)
True21
 
1.8%
(Missing)1116
98.2%
2021-01-24T11:21:28.460957image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

spleen_palpation
Boolean

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)0.3%
Missing812
Missing (%)71.4%
Memory size9.0 KiB
False
325 
(Missing)
812 
ValueCountFrequency (%)
False325
28.6%
(Missing)812
71.4%
2021-01-24T11:21:28.493485image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

treatment_haemorrhage
Boolean

MISSING

Distinct2
Distinct (%)1.4%
Missing989
Missing (%)87.0%
Memory size9.0 KiB
False
147 
True
 
1
(Missing)
989 
ValueCountFrequency (%)
False147
 
12.9%
True1
 
0.1%
(Missing)989
87.0%
2021-01-24T11:21:28.524377image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

vomiting
Boolean

MISSING

Distinct2
Distinct (%)1.4%
Missing989
Missing (%)87.0%
Memory size9.0 KiB
False
146 
True
 
2
(Missing)
989 
ValueCountFrequency (%)
False146
 
12.8%
True2
 
0.2%
(Missing)989
87.0%
2021-01-24T11:21:28.557714image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

vomiting_level
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
nan
1106 
1.0
 
18
2.0
 
12
3.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3411
Distinct characters7
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rownan
2nd rownan
3rd rownan
4th rownan
5th rownan
ValueCountFrequency (%)
nan1106
97.3%
1.018
 
1.6%
2.012
 
1.1%
3.01
 
0.1%
2021-01-24T11:21:28.711733image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-24T11:21:28.766176image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
nan1106
97.3%
1.018
 
1.6%
2.012
 
1.1%
3.01
 
0.1%

Most occurring characters

ValueCountFrequency (%)
n2212
64.8%
a1106
32.4%
.31
 
0.9%
031
 
0.9%
118
 
0.5%
212
 
0.4%
31
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3318
97.3%
Decimal Number62
 
1.8%
Other Punctuation31
 
0.9%

Most frequent character per category

ValueCountFrequency (%)
031
50.0%
118
29.0%
212
 
19.4%
31
 
1.6%
ValueCountFrequency (%)
n2212
66.7%
a1106
33.3%
ValueCountFrequency (%)
.31
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3318
97.3%
Common93
 
2.7%

Most frequent character per script

ValueCountFrequency (%)
.31
33.3%
031
33.3%
118
19.4%
212
 
12.9%
31
 
1.1%
ValueCountFrequency (%)
n2212
66.7%
a1106
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII3411
100.0%

Most frequent character per block

ValueCountFrequency (%)
n2212
64.8%
a1106
32.4%
.31
 
0.9%
031
 
0.9%
118
 
0.5%
212
 
0.4%
31
 
< 0.1%

wbc
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct92
Distinct (%)27.4%
Missing801
Missing (%)70.4%
Infinite0
Infinite (%)0.0%
Mean4.843154762
Minimum0.9
Maximum41.6
Zeros0
Zeros (%)0.0%
Memory size9.0 KiB
2021-01-24T11:21:28.858900image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.9
5-th percentile1.8
Q13
median4.3
Q35.7
95-th percentile9.6
Maximum41.6
Range40.7
Interquartile range (IQR)2.7

Descriptive statistics

Standard deviation3.137830915
Coefficient of variation (CV)0.6478898713
Kurtosis56.05571438
Mean4.843154762
Median Absolute Deviation (MAD)1.3
Skewness5.38140725
Sum1627.3
Variance9.845982854
MonotocityNot monotonic
2021-01-24T11:21:28.985164image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.211
 
1.0%
3.810
 
0.9%
2.79
 
0.8%
3.69
 
0.8%
4.58
 
0.7%
4.78
 
0.7%
3.98
 
0.7%
4.68
 
0.7%
2.48
 
0.7%
2.68
 
0.7%
Other values (82)249
 
21.9%
(Missing)801
70.4%
ValueCountFrequency (%)
0.91
0.1%
11
0.1%
1.12
0.2%
1.32
0.2%
1.41
0.1%
ValueCountFrequency (%)
41.61
0.1%
15.21
0.1%
141
0.1%
13.91
0.1%
13.71
0.1%

weight
Real number (ℝ≥0)

Distinct32
Distinct (%)2.8%
Missing3
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean54.64373898
Minimum18
Maximum110
Zeros0
Zeros (%)0.0%
Memory size9.0 KiB
2021-01-24T11:21:29.090985image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile37
Q145
median53
Q364
95-th percentile76
Maximum110
Range92
Interquartile range (IQR)19

Descriptive statistics

Standard deviation13.70236096
Coefficient of variation (CV)0.2507581147
Kurtosis2.424173705
Mean54.64373898
Median Absolute Deviation (MAD)8
Skewness0.8163935446
Sum61966
Variance187.754696
MonotocityNot monotonic
2021-01-24T11:21:29.193741image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
50116
 
10.2%
4293
 
8.2%
5580
 
7.0%
5765
 
5.7%
5260
 
5.3%
4559
 
5.2%
5657
 
5.0%
6553
 
4.7%
7051
 
4.5%
6644
 
3.9%
Other values (22)456
40.1%
ValueCountFrequency (%)
1811
 
1.0%
2515
 
1.3%
3525
2.2%
3739
3.4%
3920
1.8%
ValueCountFrequency (%)
11010
0.9%
9620
1.8%
8016
1.4%
7614
1.2%
7417
1.5%

Interactions

2021-01-24T11:20:16.721704image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:16.801723image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:16.866275image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:16.937898image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:17.014778image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:17.079171image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:17.141103image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:17.207949image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:17.282944image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:17.351757image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:17.417321image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:17.497092image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:17.669278image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:17.745639image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:17.815053image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:17.891740image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:17.969025image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:18.048737image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:18.118759image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:18.188296image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:18.266315image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:18.341522image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:18.417058image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:18.486168image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:18.561068image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:18.638096image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:18.701879image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:18.763921image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:18.832252image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:18.899262image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:18.960757image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:19.020422image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:19.077040image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:19.138539image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:19.202426image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:19.263109image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:19.331110image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:19.398135image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:19.457931image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:19.520152image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:19.587166image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:19.651830image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:19.722183image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:19.780463image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:19.842508image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:20.013917image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:20.084828image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:20.144763image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:20.207527image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:20.273298image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:20.332439image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:20.396173image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:20.455609image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:20.520346image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:20.587777image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:20.651751image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:20.712674image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:20.770765image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:20.837856image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:20.913330image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:20.978628image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:21.045052image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:21.107083image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:21.169576image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:21.237405image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:21.300380image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:21.361213image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:21.435386image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:21.514789image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:21.600623image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:21.683298image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:21.763974image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:21.840371image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:21.909261image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:21.972544image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:22.030301image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:22.099751image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:22.171998image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:22.247909image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:22.311641image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:22.375596image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:22.442904image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:22.499592image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:22.565395image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:22.633172image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:22.825649image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:22.919719image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:22.995171image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:23.066735image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:23.130186image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:23.205320image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:23.277487image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:23.355543image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:23.420129image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:23.483909image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:23.559108image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:23.626396image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:23.696572image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:23.758851image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:23.831482image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:23.898963image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:23.969379image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:24.033463image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:24.100123image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:24.162858image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:24.232861image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:24.300794image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:24.364361image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:24.440563image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:24.507919image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:24.575662image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:24.645380image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:24.719005image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:24.775607image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:24.835994image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:24.898933image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:24.962715image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:25.026022image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:25.103003image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:25.162488image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:25.232149image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:25.308408image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:25.384794image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:25.455699image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:25.518880image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:25.588351image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:25.650382image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:25.715243image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:25.784812image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:25.853941image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:25.923472image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:25.989561image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:26.048937image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:26.108676image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:26.167546image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:26.415108image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:26.496953image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:26.558161image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:26.617191image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:26.675235image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:26.737925image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:26.798487image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:26.863861image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:26.936800image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:27.001878image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:27.067562image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:27.125712image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:27.185853image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:27.254101image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:27.314866image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:27.378371image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:27.438404image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:27.498738image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:27.555800image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:27.624999image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:27.694942image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:27.758962image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:27.818477image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:27.879397image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:27.951492image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:28.016525image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:28.082895image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:28.144258image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:28.210929image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:28.277507image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:28.339783image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:28.399288image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:28.466958image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:28.536943image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:28.609346image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:28.675600image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:28.745922image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:28.820650image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:28.886012image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:28.946746image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:29.003656image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:29.067491image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:29.125124image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:29.185440image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:29.249138image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:29.313292image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:29.371822image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:29.429626image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:29.508313image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:29.578993image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:29.636512image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:29.707132image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:29.777250image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:29.834790image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:29.892908image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:29.953278image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:30.024374image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:30.090564image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:30.152669image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:30.212241image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:30.279639image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:30.344079image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:30.414213image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:30.684745image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:30.762702image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:30.832561image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:30.905483image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:30.972103image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:31.040189image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:31.107077image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:31.180478image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:31.241583image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:31.304582image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:31.373552image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:31.441329image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:31.512402image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:31.582111image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:31.656133image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:31.721670image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:31.788147image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:31.863703image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:31.937213image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:32.017503image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:32.085741image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:32.161422image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:32.240528image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:32.313211image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:32.383416image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:32.451434image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:32.526856image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:32.596767image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:32.664920image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:32.729541image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:32.802056image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:32.870850image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:32.936458image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:32.997157image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:33.066703image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:33.123939image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:33.189103image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:33.257944image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:33.331167image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:33.403590image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:33.463958image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:33.524891image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:33.591624image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:33.658493image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:33.728299image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:33.787192image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:33.844377image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:33.908682image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:33.967136image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:34.035305image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:34.099968image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:34.173903image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:34.244220image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:34.305888image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:34.365309image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:34.427517image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:34.487406image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:34.553571image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:34.623140image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:34.685800image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:34.744593image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:34.815335image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:34.880865image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:34.951936image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:35.019440image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:35.079473image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:35.140172image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:35.201728image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:35.261931image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:35.325624image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:35.384225image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:35.443050image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:35.504022image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:35.563855image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:35.627934image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:35.691041image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:36.003210image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:36.082408image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:36.153147image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:36.225857image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:36.295989image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:36.373522image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:36.444184image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:36.512385image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:36.578137image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:36.646238image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:36.715741image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:36.785889image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:36.857652image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:36.957997image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:37.026562image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:37.094243image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:37.167514image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:37.242165image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:37.317364image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:37.377651image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:37.448390image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:37.523787image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:37.598760image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:37.676714image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:37.745300image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:37.817033image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:37.888300image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:37.973888image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:38.037606image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:38.099483image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:38.168192image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:38.249132image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:38.315673image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:38.378409image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:38.449180image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:38.530326image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:38.602068image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:38.666332image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:38.741632image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:38.806060image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:38.879060image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:38.960669image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:39.042532image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:39.128272image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:39.199919image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:39.275529image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:39.357295image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:39.435607image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:39.513278image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:39.590762image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:39.674951image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:39.757599image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:39.824339image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:39.884873image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:39.947845image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:40.008378image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:40.065806image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:40.127524image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:40.191071image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:40.250700image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:40.316800image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:40.376029image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:40.435741image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:40.503073image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:40.570275image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:40.640297image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:40.705832image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:40.772175image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:40.837881image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:40.895340image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:40.966655image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:41.024613image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:41.084502image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:41.142308image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:41.200527image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:41.270703image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:41.333162image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:41.405022image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:41.468012image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:41.532876image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:41.597559image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:41.655236image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:41.715703image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:41.782809image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:41.840896image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:41.906117image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:41.965790image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:42.025475image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:42.092310image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:42.164177image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:42.236804image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:42.301025image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:42.369759image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:42.742731image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:42.820016image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:42.899194image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:42.958071image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:43.018085image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:43.076705image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:43.133682image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:43.201217image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:43.270952image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:43.349946image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:43.416891image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:43.481191image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:43.551870image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:43.614879image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:43.680325image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:43.747933image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:43.808847image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:43.888715image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:43.956323image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:44.015947image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:44.093804image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:44.175326image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:44.243184image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:44.308373image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:44.396971image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:44.482611image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:44.543039image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:44.612170image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:44.696725image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:44.763671image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:44.836190image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:44.896632image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:44.979157image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:45.053478image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:45.137793image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:45.204711image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:45.266256image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:45.336779image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:45.398940image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:45.460086image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:45.519921image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:45.593204image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:45.666303image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:45.730787image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:45.789009image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:45.862639image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:45.940344image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:46.008940image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:46.077369image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:46.158232image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:46.240981image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:46.307235image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:46.383164image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:46.461030image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:46.523326image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:46.588939image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:46.650550image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:46.731320image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:46.805108image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:46.886115image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:46.958901image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:47.029874image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:47.107343image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:47.167808image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:47.234501image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:47.303600image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:47.369555image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:47.453179image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:47.525366image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:47.593467image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:47.677640image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:47.765401image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:47.834504image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:47.904008image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:47.988718image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:48.072791image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:48.137335image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:48.204461image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:48.294698image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:48.361813image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:48.431527image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:48.492713image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:48.582130image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:48.665370image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:48.738445image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:48.800260image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:48.864864image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:48.929245image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:49.004162image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:49.064977image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:49.131868image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:49.192232image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:49.259203image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:49.319928image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:49.381315image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:49.444991image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:49.515190image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:49.575191image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:49.636502image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:49.696568image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:49.759925image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:49.818936image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:49.878190image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:49.937852image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:50.013568image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:50.088453image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:50.155984image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:50.222719image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:50.295730image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:50.368458image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:50.434004image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:50.512581image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:50.592447image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:50.657645image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:51.096161image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:51.187707image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:51.247612image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:51.327910image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:51.386825image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:51.444829image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:51.516922image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:51.590677image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:51.665423image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:51.744746image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:51.812079image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:51.884852image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:51.949039image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:52.007999image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:52.079928image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:52.145185image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:52.208834image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:52.272331image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:52.347460image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:52.418777image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:52.498037image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:52.565928image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:52.632008image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:52.708836image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:52.773527image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:52.838141image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:52.902911image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:52.968646image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:53.046575image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:53.110899image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:53.176738image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:53.255183image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:53.335291image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:53.392230image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:53.447931image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:53.527895image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:53.606264image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:53.691607image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:53.752902image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:53.826050image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:53.894184image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:53.967564image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:54.029589image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:54.109932image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:54.186960image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:54.263672image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:54.318001image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:54.389346image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:54.455236image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:54.531454image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:54.585785image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:54.655753image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:54.723760image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:54.795402image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:54.852624image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:54.911744image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:54.983052image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:55.062676image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:55.121304image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:55.181118image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:55.251766image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:55.317671image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:55.382798image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:55.456344image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:55.513688image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:55.580721image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:55.660559image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:55.735161image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:55.801786image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:55.877238image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:55.952773image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:56.012445image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:56.086472image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:56.155877image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:56.234419image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:56.293228image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:56.366802image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:56.439781image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:56.512492image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:56.577560image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:56.642639image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:56.721517image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:56.799767image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:56.856305image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:56.914492image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:56.986044image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:57.054410image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:57.124297image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:57.201159image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:57.262665image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:57.336907image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:57.416095image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:57.488627image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:57.556033image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:57.626152image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:57.699195image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:57.762659image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:57.826840image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:57.887841image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:57.956851image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:58.019719image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:58.083080image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:58.151998image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:58.224649image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:58.288275image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:58.349662image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:58.414928image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:58.488417image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:58.544787image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:58.603375image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:58.664481image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:58.726551image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:58.785511image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:58.848094image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:58.905752image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:58.966780image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:59.036933image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:59.107411image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:59.164004image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:59.234445image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:59.313345image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:59.378651image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:59.442192image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:59.514303image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:59.574056image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:59.632193image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:59.696642image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:59.755270image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:59.829146image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:59.902988image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:20:59.971048image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:21:00.045053image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:21:00.128303image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:21:00.201218image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:21:00.269737image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:21:00.352566image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:21:00.430593image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:21:00.512850image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:21:00.575627image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:21:00.649324image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:21:01.207027image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:21:01.291346image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:21:01.359402image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:21:01.417054image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:21:01.495849image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:21:01.572581image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:21:01.631783image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:21:01.691627image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:21:01.756383image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:21:01.823866image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:21:01.882374image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:21:01.937923image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:21:02.008740image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:21:02.077646image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:21:02.144673image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:21:02.210888image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:21:02.280151image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:21:02.361148image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:21:02.420599image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:21:02.485671image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:21:02.558804image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:21:02.629937image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:21:02.707779image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:21:02.781339image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:21:02.848542image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:21:02.922062image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:21:02.995486image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:21:03.066855image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-24T11:21:03.133364image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2021-01-24T11:21:29.352119image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-01-24T11:21:29.636476image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-01-24T11:21:29.912870image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-01-24T11:21:30.292607image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-01-24T11:21:03.692145image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-01-24T11:21:06.780703image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-01-24T11:21:09.265951image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-01-24T11:21:14.022702image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

study_nodateabdominal_pain_levelabdominal_tendernessagealbuminaltanemiaanorexiaantibioticsantibiotics_listascitesastasthmableedingbleeding_gumbleeding_nosebleeding_otherbleeding_skinbleeding_vaginalblood_fluidbody_temperaturebruisingchillschronic_hepatitiscolloidcolloid_descriptionconjunctival_injectionconvulsionscoronary_heart_diseasecoughcreatine_kinasecreatininecrystalloidcrystalloid_descriptiondbpdiabetesdiarrhoeaevent_enrolmentevent_feverevent_pcrfeeling_faintfluid_reason_otherfluid_reason_other_descriptionfluid_stategenderhaematocrit_highhaematocrit_percent_labhaematocrit_percent_maxhaematocrit_percent_minhaemoglobinheadache_levelheart_soundheighthematemesishematuriahypertensioniggigg_interpretationigmigm_interpretationjaundicejoint_pain_levellethargyliver_palpationliver_sizelymphadenopathylymphadenopathy_specificationlymphocytes_percentmedicationmedication_listmelaenameningismmonocytes_percentmuscle_pain_levelnauseaneurology_abnormalneutrophils_percentoedemaother_blood_productparental_fluidparental_fluid_volumepcr_dengue_interpretationpcr_dengue_loadpcr_dengue_serotypepeptic_ulcerpetechiaepharyngeal_injectionplatelet_minplateletspleural_effusionpregnantpulserales_cracklesrehydrationrenal_diseaserespiratory_distressrespiratory_raterestlessnessretro_painrhinitissbpserology_interpretationshock_clinicalshock_resucitationskin_describeskin_flushskin_rashsore_throatspleen_palpationtreatment_haemorrhagevomitingvomiting_levelwbcweight
020 - 07622013-11-16 00:00:00NaNNaN19.0NaNNaNFalseTrueNaNNaNNaNNaNFalseNaNNaNNaNNaNFalseNaNNaNNaNNaNTrueFalseNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseNaNNaNNaNNaNNaNNaNNaNNaNMaleNaNNaNNaNNaNNaN2.0NaN180.0NaNNaNNaNNaNNaNNaNNaNNaN1.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.0NaNNaNNaNNaNNaNNaNNaNLab-confirmed DengueNaN<LODFalseNaNNaNNaNNaNNaNFalseNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN56.0
120 - 07622013-11-16 13:00:00NaNNaN19.0NaNNaNFalseTrueNaNNaNNaNNaNFalseNaNNaNNaNNaNFalseNaNNaNNaNNaNNaNFalseNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseNaNNaNTrueNaNNaNNaNNaNNaNMaleNaNNaNNaNNaNNaNNaNNaN180.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNLab-confirmed DengueNaN<LODFalseNaNNaNNaNNaNNaNFalseNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN56.0
220 - 07622013-11-18 06:08:00NaNNaN19.0NaN26.0FalseTrueNaNNaNNaN32.0FalseNaNNaNNaNNaNFalseNaNNaNNaNNaNNaNFalseNaNNaNNaNNaNNaNNaNNaN106.0NaNNaNNaNFalseNaNNaNNaNNaNNaNNaNNaNNaNMaleNaN42.0NaNNaN13.8NaNNaN180.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN19.5NaNNaNNaNNaN18.4NaNNaNNaN61.7NaNNaNNaNNaNLab-confirmed DengueNaN<LODFalseNaNNaN142.0NaNNaNFalseNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN4.656.0
320 - 07622013-11-18 14:00:00NaNFalse19.0NaNNaNFalseTrueNaNNaNFalseNaNFalseNaNFalseFalseFalseFalseNaNNaN38.5FalseNaNFalseNaNNaNTrueNaNFalseNaNNaNNaNNaNNaNNaNFalseNaNTrueNaNNaNNaNNaNNaNNaNMaleNaNNaNNaNNaNNaNNaNTrue180.0FalseFalseFalseNaNNaNNaNNaNFalseNaNFalseFalseNaNFalseNaNNaNNaNNaNFalseNaNNaNNaNNaNFalseNaNFalseNaNNaNNaNLab-confirmed DengueNaN<LODFalseFalseFalseNaNNaNFalseFalse67.0FalseNaNFalseFalse20.0FalseNaNFalse61.0NaNNaNNaNNaNTrueFalseNaNFalseNaNNaNNaNNaN56.0
420 - 07622013-11-19 05:00:00NaNNaN19.0NaNNaNFalseTrueNaNNaNNaNNaNFalseNaNNaNNaNNaNFalseNaNNaNNaNNaNNaNFalseNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseNaNNaNNaNNaNNaNNaNNaNNaNMaleNaNNaNNaNNaNNaNNaNNaN180.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNLab-confirmed DengueNaN<LODFalseNaNNaNNaNNaNNaNFalseNaNNaNNaNNaNNaNNaNNaNTrueNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN56.0
520 - 07622013-11-19 06:22:00NaNNaN19.0NaNNaNFalseTrueNaNNaNNaNNaNFalseNaNNaNNaNNaNFalseNaNNaNNaNNaNNaNFalseNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseNaNNaNNaNNaNNaNNaNNaNNaNMaleNaN45.4NaNNaN14.8NaNNaN180.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN26.0NaNNaNNaNNaN13.9NaNNaNNaN59.7NaNNaNNaNNaNLab-confirmed DengueNaN<LODFalseNaNNaN114.0NaNNaNFalseNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2.256.0
620 - 07622013-11-19 09:00:00NaNFalse19.0NaNNaNFalseTrueFalseNaNFalseNaNFalseTrueTrueFalseNaNFalseFalseNaN39.0TrueNaNFalseNaNNaNTrueFalseNaNNaNNaNNaNNaNNaNNaNFalseNaNNaNNaNNaNNaNNaNNaNNaNMaleNaNNaNNaNNaNNaNNaNNaN180.0FalseFalseNaNNaNNaNNaNNaNFalseNaNFalseFalseNaNFalseNaNNaNFalseNaNFalseFalseNaNNaNNaNFalseNaNFalseNaNNaNNaNLab-confirmed DengueNaN<LODFalseFalseFalseNaNNaNFalseFalse64.0FalseNaNNaNFalse20.0FalseNaNFalse121.0NaNFalseNaNNaNTrueFalseNaNFalseNaNNaNNaNNaN56.0
720 - 07622013-11-20 07:00:00NaNNaN19.0NaNNaNFalseTrueNaNNaNNaNNaNFalseNaNNaNNaNNaNFalseNaNNaNNaNNaNNaNFalseNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseNaNNaNNaNNaNNaNNaNNaNNaNMaleNaN45.6NaNNaN14.9NaNNaN180.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN23.3NaNNaNNaNNaN13.7NaNNaNNaN62.1NaNNaNNaNNaNLab-confirmed DengueNaN<LODFalseNaNNaN84.0NaNNaNFalseNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2.856.0
820 - 07622013-11-20 14:00:00NaNFalse19.0NaNNaNFalseTrueFalseNaNFalseNaNFalseTrueFalseFalseNaNFalseFalseNaN39.5TrueNaNFalseNaNNaNTrueFalseNaNNaNNaNNaNNaNNaNNaNFalseNaNNaNNaNNaNNaNNaNNaNNaNMaleNaNNaNNaNNaNNaNNaNNaN180.0FalseFalseNaNNaNNaNNaNNaNFalseNaNFalseFalseNaNFalseNaNNaNFalseNaNFalseFalseNaNNaNNaNFalseNaNFalseNaNNaNNaNLab-confirmed DengueNaN<LODFalseFalseFalseNaNNaNFalseFalse65.0FalseNaNNaNFalse20.0FalseNaNFalse120.0NaNFalseNaNNaNTrueFalseNaNFalseNaNNaNNaNNaN56.0
920 - 07622013-11-20 19:00:00NaNNaN19.0NaNNaNFalseTrueNaNNaNNaNNaNFalseNaNNaNNaNNaNFalseNaNNaNNaNNaNNaNFalseNaNNaNNaNNaNNaNTrueNaNNaNNaNNaNNaNFalseNaNNaNNaNNaNNaNNaNNaNNaNMaleNaNNaNNaNNaNNaNNaNNaN180.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNLab-confirmed DengueNaN<LODFalseNaNNaNNaNNaNNaNFalseNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN56.0

Last rows

study_nodateabdominal_pain_levelabdominal_tendernessagealbuminaltanemiaanorexiaantibioticsantibiotics_listascitesastasthmableedingbleeding_gumbleeding_nosebleeding_otherbleeding_skinbleeding_vaginalblood_fluidbody_temperaturebruisingchillschronic_hepatitiscolloidcolloid_descriptionconjunctival_injectionconvulsionscoronary_heart_diseasecoughcreatine_kinasecreatininecrystalloidcrystalloid_descriptiondbpdiabetesdiarrhoeaevent_enrolmentevent_feverevent_pcrfeeling_faintfluid_reason_otherfluid_reason_other_descriptionfluid_stategenderhaematocrit_highhaematocrit_percent_labhaematocrit_percent_maxhaematocrit_percent_minhaemoglobinheadache_levelheart_soundheighthematemesishematuriahypertensioniggigg_interpretationigmigm_interpretationjaundicejoint_pain_levellethargyliver_palpationliver_sizelymphadenopathylymphadenopathy_specificationlymphocytes_percentmedicationmedication_listmelaenameningismmonocytes_percentmuscle_pain_levelnauseaneurology_abnormalneutrophils_percentoedemaother_blood_productparental_fluidparental_fluid_volumepcr_dengue_interpretationpcr_dengue_loadpcr_dengue_serotypepeptic_ulcerpetechiaepharyngeal_injectionplatelet_minplateletspleural_effusionpregnantpulserales_cracklesrehydrationrenal_diseaserespiratory_distressrespiratory_raterestlessnessretro_painrhinitissbpserology_interpretationshock_clinicalshock_resucitationskin_describeskin_flushskin_rashsore_throatspleen_palpationtreatment_haemorrhagevomitingvomiting_levelwbcweight
112720-07742013-12-23 09:30:00NaNNaN53.040.040.0FalseTrueNaNNaNNaN26.0FalseNaNNaNNaNNaNFalseNaNNaNNaNNaNNaNFalseNaNNaNNaNNaNNaNNaN49.074.0NaNNaNNaNFalseNaNNaNNaNNaNNaNNaNNaNNaNMaleNaN54.0NaNNaN17.1NaNNaN160.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN37.3NaNNaNNaNNaN9.8NaNNaNNaN49.0NaNNaNNaNNaNLab-confirmed DengueNaN<LODFalseNaNNaN323.0NaNNaNFalseNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN7.365.0
112820-07752013-12-13 00:00:00NaNNaN34.0NaNNaNFalseFalseNaNNaNNaNNaNFalseNaNNaNNaNNaNFalseNaNNaNNaNNaNTrueFalseNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseNaNNaNNaNNaNNaNNaNNaNNaNFemaleNaNNaNNaNNaNNaN1.0NaN158.0NaNNaNNaNNaNNaNNaNNaNNaN1.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.0NaNNaNNaNNaNNaNNaNNaNLab-confirmed DengueNaN<LODFalseNaNNaNNaNNaNNaNFalseNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN50.0
112920-07752013-12-13 20:00:00NaNNaN34.0NaNNaNFalseFalseNaNNaNNaNNaNFalseNaNNaNNaNNaNFalseNaNNaNNaNNaNNaNFalseNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseNaNNaNTrueNaNNaNNaNNaNNaNFemaleNaNNaNNaNNaNNaNNaNNaN158.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNLab-confirmed DengueNaN<LODFalseNaNNaNNaNNaNNaNFalseNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN50.0
113020-07752013-12-18 00:00:00NaNNaN34.0NaNNaNFalseFalseNaNNaNNaNNaNFalseNaNNaNNaNNaNFalseNaNNaNNaNNaNNaNFalseNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseNaNNaNNaNTrueNaNNaNNaNNaNFemaleNaNNaNNaNNaNNaNNaNNaN158.0NaNNaNNaN14.08Negative29.2PositiveNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNLab-confirmed Dengue0.0<LODFalseNaNNaNNaNNaNNaNFalseNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNInconclusiveNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN50.0
113120-07752013-12-18 08:18:00NaNNaN34.0NaN20.0FalseFalseNaNNaNNaN24.0FalseNaNNaNNaNNaNFalseNaNNaNNaNNaNNaNFalseNaNNaNNaNNaNNaNNaNNaN70.0NaNNaNNaNFalseNaNNaNNaNNaNNaNNaNNaNNaNFemaleNaN40.5NaNNaN13.3NaNNaN158.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN29.2NaNNaNNaNNaN20.1NaNNaNNaN39.9NaNNaNNaNNaNLab-confirmed DengueNaN<LODFalseNaNNaN120.0NaNNaNFalseNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2.550.0
113220-07752013-12-18 15:00:00NaNFalse34.0NaNNaNFalseFalseNaNNaNFalseNaNFalseNaNFalseFalseFalseFalseFalseNaN38.5TrueNaNFalseNaNNaNFalseNaNFalseNaNNaNNaNNaNNaNNaNFalseNaNTrueNaNNaNNaNNaNNaNNaNFemaleNaNNaNNaNNaNNaNNaNTrue158.0FalseFalseFalseNaNNaNNaNNaNFalseNaNFalseFalseNaNFalseNaNNaNNaNNaNFalseNaNNaNNaNNaNFalseNaNFalseNaNNaNNaNLab-confirmed DengueNaN<LODFalseFalseFalseNaNNaNFalseFalse79.0FalseNaNFalseFalse20.0FalseNaNNaN58.0NaNNaNNaNMACULARTrueTrueNaNFalseNaNNaNNaNNaN50.0
113320-07752013-12-19 05:59:00NaNNaN34.0NaNNaNFalseFalseNaNNaNNaNNaNFalseNaNNaNNaNNaNFalseNaNNaNNaNNaNNaNFalseNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseNaNNaNNaNNaNNaNNaNNaNNaNFemaleNaN38.7NaNNaN13.0NaNNaN158.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN42.7NaNNaNNaNNaN20.8NaNNaNNaN35.7NaNNaNNaNNaNLab-confirmed DengueNaN<LODFalseNaNNaN113.0NaNNaNFalseNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN3.250.0
113420-07752013-12-19 10:00:00NaNFalse34.0NaNNaNFalseFalseFalseNaNFalseNaNFalseTrueTrueFalseNaNFalseFalseNaN36.8TrueNaNFalseNaNNaNFalseFalseNaNNaNNaNNaNNaNNaN60.0FalseNaNNaNNaNNaNNaNNaNNaNNaNFemaleNaNNaNNaNNaNNaNNaNNaN158.0FalseFalseNaNNaNNaNNaNNaNFalseNaNFalseFalseNaNFalseNaNNaNFalseNaNFalseFalseNaNNaNNaNFalseNaNFalseNaNNaNNaNLab-confirmed DengueNaN<LODFalseFalseTrueNaNNaNFalseFalse80.0FalseNaNNaNFalse20.0FalseNaNFalse100.0NaNFalseNaNNaNTrueTrueNaNFalseNaNNaNNaNNaN50.0
113520-07752013-12-20 05:47:00NaNNaN34.0NaNNaNFalseFalseNaNNaNNaNNaNFalseNaNNaNNaNNaNFalseNaNNaNNaNNaNNaNFalseNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseNaNNaNNaNNaNNaNNaNNaNNaNFemaleNaN38.7NaNNaN12.9NaNNaN158.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN41.3NaNNaNNaNNaN20.7NaNNaNNaN36.7NaNNaNNaNNaNLab-confirmed DengueNaN<LODFalseNaNNaN135.0NaNNaNFalseNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN5.450.0
113620-07752013-12-20 09:00:00NaNFalse34.0NaNNaNFalseFalseFalseNaNFalseNaNFalseTrueFalseFalseNaNFalseFalseNaN37.0TrueNaNFalseNaNNaNFalseFalseNaNNaNNaNNaNNaNNaNNaNFalseNaNNaNNaNNaNNaNNaNNaNNaNFemaleNaNNaNNaNNaNNaNNaNNaN158.0FalseFalseNaNNaNNaNNaNNaNFalseNaNFalseFalseNaNFalseNaNNaNFalseNaNFalseFalseNaNNaNNaNFalseNaNFalseNaNNaNNaNLab-confirmed DengueNaN<LODFalseFalseFalseNaNNaNFalseFalse78.0FalseNaNNaNFalse20.0FalseNaNFalse100.0NaNFalseNaNNaNTrueTrueNaNFalseNaNNaNNaNNaN50.0